
@article{viering_shape_2023,
	title = {The shape of learning curves: a review},
	volume = {45},
	issn = {0162-8828},
	shorttitle = {The shape of learning curves},
	abstract = {Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning. This review recounts the origins of the term, provides a formal definition of the learning curve, and briefly covers basics such as its estimation. Our main contribution is a comprehensive overview of the literature regarding the shape of learning curves. We discuss empirical and theoretical evidence that supports well-behaved curves that often have the shape of a power law or an exponential. We consider the learning curves of Gaussian processes, the complex shapes they can display, and the factors influencing them. We draw specific attention to examples of learning curves that are ill-behaved, showing worse learning performance with more training data. To wrap up, we point out various open problems that warrant deeper empirical and theoretical investigation. All in all, our review underscores that learning curves are surprisingly diverse and no universal model can be identified.},
	language = {English},
	number = {06},
	urldate = {2026-05-04},
	journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
	author = {Viering, Tom and Loog, Marco},
	month = jun,
	year = {2023},
	note = {{doi}:10.1109/TPAMI.2022.3220744},
	pages = {7799--7819},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/ERHV5XMJ/Viering and Loog - 2021 - The Shape of Learning Curves a Review.pdf:application/pdf},
}

@article{kapoor_leakage_2023,
	title = {Leakage and the reproducibility crisis in machine-learning-based science},
	volume = {4},
	issn = {2666-3899},
	abstract = {Machine-learning (ML) methods have gained prominence in the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. We systematically investigate reproducibility issues in ML-based science. Through a survey of literature in fields that have adopted ML methods, we find 17 fields where leakage has been found, collectively affecting 294 papers and, in some cases, leading to wildly overoptimistic conclusions. Based on our survey, we introduce a detailed taxonomy of eight types of leakage, ranging from textbook errors to open research problems. We propose that researchers test for each type of leakage by filling out model info sheets, which we introduce. Finally, we conduct a reproducibility study of civil war prediction, where complex ML models are believed to vastly outperform traditional statistical models such as logistic regression (LR). When the errors are corrected, complex ML models do not perform substantively better than decades-old LR models.},
	number = {9},
	urldate = {2026-05-04},
	journal = {Patterns},
	author = {Kapoor, Sayash and Narayanan, Arvind},
	month = sep,
	year = {2023},
	note = {{doi}:10.1016/j.patter.2023.100804},
	keywords = {leakage, machine learning, reproducibility},
	file = {ScienceDirect Full Text PDF:/Users/hanzhangren/Zotero/storage/2KJ58PFI/Kapoor and Narayanan - 2023 - Leakage and the reproducibility crisis in machine-learning-based science.pdf:application/pdf;ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/3NYQJG3P/S2666389923001599.html:text/html},
}

@article{ludwig_machine_2024,
	title = {Machine learning as a tool for hypothesis generation},
	volume = {139},
	issn = {0033-5533},
	abstract = {While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (e.g., race) or existing psychology research, nor are they already known (even if tacitly) to people or experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (e.g., cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science.},
	number = {2},
	urldate = {2026-05-04},
	journal = {Q J Econ},
	author = {Ludwig, Jens and Mullainathan, Sendhil},
	month = may,
	year = {2024},
	note = {{doi}:10.1093/qje/qjad055},
	pages = {751--827},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/TL6RC5ND/Ludwig and Mullainathan - 2024 - Machine Learning as a Tool for Hypothesis Generation.pdf:application/pdf},
}

@misc{stulp_guide_2024,
	title = {A guide on {PreFer} datasets [{LISS}]},
	abstract = {Here is some useful information that can help navigate the PreFer datasets from the LISS panel.},
	language = {en},
	urldate = {2025-08-23},
	author = {Stulp, Gert and Sivak, Elizaveta},
	month = mar,
	year = {2024},
	note = {{https}://stulp.gmw.rug.nl/prefer/posts/posts/2024-03-20-prefer-datasets.html. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/95U8CYM3/2024-03-20-prefer-datasets.html:text/html},
}

@article{lundberg_origins_2024,
	title = {The origins of unpredictability in life outcome prediction tasks},
	volume = {121},
	abstract = {Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.},
	number = {24},
	urldate = {2026-05-04},
	journal = {Proc. Natl. Acad. Sci. U. S. A.},
	author = {Lundberg, Ian and Brown-Weinstock, Rachel and Clampet-Lundquist, Susan and Pachman, Sarah and Nelson, Timothy J. and Yang, Vicki and Edin, Kathryn and Salganik, Matthew J.},
	month = jun,
	year = {2024},
    note = {{doi}:10.1073/pnas.2322973121},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/DQC8I9ML/Lundberg et al. - 2024 - The origins of unpredictability in life outcome prediction tasks.pdf:application/pdf},
}

@article{salganik_measuring_2020,
	title = {Measuring the predictability of life outcomes with a scientific mass collaboration},
	volume = {117},
	abstract = {How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.},
	number = {15},
	urldate = {2026-05-04},
	journal = {Proc. Natl. Acad. Sci. U.S.A.},
	author = {Salganik, Matthew J. and Lundberg, Ian and Kindel, Alexander T. and Ahearn, Caitlin E. and Al-Ghoneim, Khaled and Almaatouq, Abdullah and Altschul, Drew M. and Brand, Jennie E. and Carnegie, Nicole Bohme and Compton, Ryan James and Datta, Debanjan and Davidson, Thomas and Filippova, Anna and Gilroy, Connor and Goode, Brian J. and Jahani, Eaman and Kashyap, Ridhi and Kirchner, Antje and McKay, Stephen and Morgan, Allison C. and Pentland, Alex and Polimis, Kivan and Raes, Louis and Rigobon, Daniel E. and Roberts, Claudia V. and Stanescu, Diana M. and Suhara, Yoshihiko and Usmani, Adaner and Wang, Erik H. and Adem, Muna and Alhajri, Abdulla and AlShebli, Bedoor and Amin, Redwane and Amos, Ryan B. and Argyle, Lisa P. and Baer-Bositis, Livia and Büchi, Moritz and Chung, Bo-Ryehn and Eggert, William and Faletto, Gregory and Fan, Zhilin and Freese, Jeremy and Gadgil, Tejomay and Gagné, Josh and Gao, Yue and Halpern-Manners, Andrew and Hashim, Sonia P. and Hausen, Sonia and He, Guanhua and Higuera, Kimberly and Hogan, Bernie and Horwitz, Ilana M. and Hummel, Lisa M. and Jain, Naman and Jin, Kun and Jurgens, David and Kaminski, Patrick and Karapetyan, Areg and Kim, E. H. and Leizman, Ben and Liu, Naijia and Möser, Malte and Mack, Andrew E. and Mahajan, Mayank and Mandell, Noah and Marahrens, Helge and Mercado-Garcia, Diana and Mocz, Viola and Mueller-Gastell, Katariina and Musse, Ahmed and Niu, Qiankun and Nowak, William and Omidvar, Hamidreza and Or, Andrew and Ouyang, Karen and Pinto, Katy M. and Porter, Ethan and Porter, Kristin E. and Qian, Crystal and Rauf, Tamkinat and Sargsyan, Anahit and Schaffner, Thomas and Schnabel, Landon and Schonfeld, Bryan and Sender, Ben and Tang, Jonathan D. and Tsurkov, Emma and van Loon, Austin and Varol, Onur and Wang, Xiafei and Wang, Zhi and Wang, Julia and Wang, Flora and Weissman, Samantha and Whitaker, Kirstie and Wolters, Maria K. and Woon, Wei Lee and Wu, James and Wu, Catherine and Yang, Kengran and Yin, Jingwen and Zhao, Bingyu and Zhu, Chenyun and Brooks-Gunn, Jeanne and Engelhardt, Barbara E. and Hardt, Moritz and Knox, Dean and Levy, Karen and Narayanan, Arvind and Stewart, Brandon M. and Watts, Duncan J. and McLanahan, Sara},
	month = apr,
	year = {2020},
    note = {{doi}:10.1073/pnas.1915006117},
	pages = {8398--8403},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/NKHM8W4S/Salganik et al. - 2020 - Measuring the predictability of life outcomes with a scientific mass collaboration.pdf:application/pdf},
}

@article{sivak_introduction_2026,
	title = {Introduction to the special issue on the {PreFer} data challenge},
    journal = {},
	author = {Sivak, Elizaveta and Stulp, Gert},
	year = {2026},
	note = {(working paper)},
	file = {Introduction to the special issue:/Users/hanzhangren/Zotero/storage/BXY3XC7B/Introduction to the special issue.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document},
}

@article{kapoor_reforms_2024,
	title = {{REFORMS}: {consensus}-based recommendations for machine-learning-based science},
	volume = {10},
	issn = {2375-2548},
	shorttitle = {{REFORMS}},
	abstract = {Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.},
	number = {18},
	urldate = {2026-05-04},
	journal = {Sci. Adv.},
	author = {Kapoor, Sayash and Cantrell, Emily M. and Peng, Kenny and Pham, Thanh Hien and Bail, Christopher A. and Gundersen, Odd Erik and Hofman, Jake M. and Hullman, Jessica and Lones, Michael A. and Malik, Momin M. and Nanayakkara, Priyanka and Poldrack, Russell A. and Raji, Inioluwa Deborah and Roberts, Michael and Salganik, Matthew J. and Serra-Garcia, Marta and Stewart, Brandon M. and Vandewiele, Gilles and Narayanan, Arvind},
	month = may,
	year = {2024},
    note = {{doi}:10.1126/sciadv.adk3452},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/M3EWK925/Kapoor et al. - 2024 - REFORMS Consensus-based recommendations for machine-learning-based science.pdf:application/pdf},
}

@misc{luzi_recommended_2007,
	title = {Recommended practices for editing and imputation in cross-sectional business surveys},
	abstract = {PDF {\textbar} On Jan 1, 2007, O. Luzi and others published Recommended practices for editing and imputation in cross-sectional business surveys {\textbar} Find, read and cite all the research you need on ResearchGate},
	language = {en},
	urldate = {2025-08-23},
	author = {Luzi, Orietta and di Zio, Marco and Guarnera, Ugo and Manzari, Antonia and de Wall, Ton and Pannekoek, Jeroen and Hoogland, Jeffrey and Templeman, Caren and Hullinger, Beat and Kilchmann, Daniel},
	year = {2007},
	note = {{https}://ec.europa.eu/eurostat/documents/64157/4374310/30-Recommended+Practices-for-editing-and-imputation-in-cross-sectional-business-surveys-2008.pdf. Accessed 24 Jul 2026},
}

@inproceedings{chen_xgboost_2016,
	address = {New York},
	title = {{XGBoost}: a scalable tree boosting system},
	isbn = {978-1-4503-4232-2},
	shorttitle = {{XGBoost}},
	abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.},
	urldate = {2026-05-07},
	booktitle = {{KDD} '16: proceedings of the 22nd {ACM} {SIGKDD} international conference on knowledge discovery and data mining},
	publisher = {Association for Computing Machinery},
	author = {Chen, Tianqi and Guestrin, Carlos},
	editor = {Krishnapuram, Balaji and Shah, Mohak and Smola, Alexander J. and Aggarwal, Charu and Shen, Dou and Rastogi, Rajeev},
	month = aug,
	year = {2016},
	pages = {785--794},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/ZYGVEZEC/Chen and Guestrin - 2016 - XGBoost A Scalable Tree Boosting System.pdf:application/pdf},
}

@misc{van_der_laan_person_2022,
	title = {A person network of the {Netherlands}},
	abstract = {As part of its research programme Statistics Netherlands has derived a network of the entire Dutch population. This paper describes the methods used to derive the network and will give an overview of some of its properties.},
	language = {en-GB},
	urldate = {2025-08-23},
	author = {van der Laan, D. J.},
	month = may,
	year = {2022},
	note = {{https}://www.cbs.nl/en-gb/background/2022/20/a-person-network-of-the-netherlands. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/CV6AKPJM/a-person-network-of-the-netherlands.html:text/html},
}

@article{van_tintelen_explaining_2024,
	title = {Explaining uncertainty in women's fertility preferences},
	volume = {10},
	issn = {2405-8440},
	abstract = {People's fertility preferences are often considered an important determinant of fertility. What is often neglected in studies of preferred fertility, is the uncertainty that people may have about their preferences. In this study, using data on Dutch women through the Longitudinal Internet studies for the Social Sciences (collected early 2018), we examined women's fertility preferences and asked detailed questions about the certainty of these preferences. We also examined whether women agreed with their partner on preferred family size, and to what extent partner (dis)agreement shaped uncertainty. We show that Dutch women expressed much uncertainty about their fertility preferences, with only one-third feeling strongly about their preferences. Uncertainty strongly increased when women preferred higher numbers of children, whereas already having children reduced it. Women who wanted no children were most certain about their preference. Higher preferred family sizes also led to more disagreement with the partner about these preferences, and greater partner disagreement, in turn, led to more uncertainty. These findings imply that people are more likely to downgrade their fertility preferences than to increase them, as women are more certain about their preferences for lower numbers of children and are more open to family sizes below than above their preferred choice. Partner disagreement is often resolved by not having (more) children, lowering realised fertility. Hence, these findings provide another explanation for why many people have fewer children than desired.},
	number = {6},
	urldate = {2025-08-23},
	journal = {Heliyon},
	author = {van Tintelen, Amke M. G. and Stulp, Gert},
	month = mar,
	year = {2024},
	note = {{doi}:10.1016/j.heliyon.2024.e27610},
	keywords = {Family size, Fertility preferences, Partner agreement, The Netherlands, Uncertainty},
	file = {ScienceDirect Full Text PDF:/Users/hanzhangren/Zotero/storage/FYYSTKHL/van Tintelen and Stulp - 2024 - Explaining uncertainty in women's fertility preferences.pdf:application/pdf;ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/LWGYVZE6/S2405844024036417.html:text/html},
}

@inproceedings{yao_wild-time_2023,
	address = {Red Hook},
	title = {Wild-time: a benchmark of in-the-wild distribution shift over time},
	volume = {14},
	shorttitle = {Wild-time},
	language = {en},
	urldate = {2025-10-31},
	booktitle = {Advances in neural information processing systems 35: 36th conference on neural information processing systems ({NeurIPS} 2022)},
	publisher = {Curran Associates},
	author = {Yao, Huaxiu and Choi, Caroline and Cao, Bochuan and Lee, Yoonho and Koh, Pang Wei W. and Finn, Chelsea},
	editor = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.},
	year = {2023},
	pages = {10309--10324},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/A3NEQFWM/Yao et al. - 2022 - Wild-Time A Benchmark of in-the-Wild Distribution Shift over Time.pdf:application/pdf},
}

@article{kindel_improving_2019,
	title = {Improving metadata infrastructure for complex surveys: insights from the {Fragile} {Families} {Challenge}},
	volume = {5},
	issn = {2378-0231},
	shorttitle = {Improving metadata infrastructure for complex surveys},
	abstract = {Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.},
	language = {EN},
	urldate = {2025-08-23},
	journal = {Socius},
	author = {Kindel, Alexander T. and Bansal, Vineet and Catena, Kristin D. and Hartshorne, Thomas H. and Jaeger, Kate and Koffman, Dawn and McLanahan, Sara and Phillips, Maya and Rouhani, Shiva and Vinh, Ryan and Salganik, Matthew J.},
	month = jan,
	year = {2019},
	note = {{doi}:10.1177/2378023118817378},
	file = {SAGE PDF Full Text:/Users/hanzhangren/Zotero/storage/DIGGWDAD/Kindel et al. - 2019 - Improving Metadata Infrastructure for Complex Surveys Insights from the Fragile Families Challenge.pdf:application/pdf},
}

@article{kessler_developing_2017,
	title = {Developing a practical suicide risk prediction model for targeting high-risk patients in the {Veterans} health {Administration}},
	volume = {26},
	copyright = {Copyright © 2017 John Wiley \& Sons, Ltd.},
	issn = {1557-0657},
	abstract = {Objectives The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here. Methods A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009–2011 who used VHA services the year of their death or prior year and a 1\% probability sample of time-matched VHA service users alive at the index date (n = 2,112,008). Results A penalized logistic model with 61 predictors had sensitivity comparable to the proof-of-concept model (which had 381 predictors) at target thresholds. The machine learning algorithms had relatively similar sensitivities, the highest being for Bayesian additive regression trees, with 10.7\% of suicides occurred among the 1.0\% of Veterans with highest predicted risk and 28.1\% among the 5.0\% of with highest predicted risk. Conclusions Based on these results, VHA is using penalized logistic regression in initial intervention implementation. The paper concludes with a discussion of other practical issues that might be explored to increase model performance.},
	language = {en},
	number = {3},
	urldate = {2025-08-23},
	journal = {Int. J. Methods Psychiatr. Res.},
	author = {Kessler, Ronald C. and Hwang, Irving and Hoffmire, Claire A. and McCarthy, John F. and Petukhova, Maria V. and Rosellini, Anthony J. and Sampson, Nancy A. and Schneider, Alexandra L. and Bradley, Paul A. and Katz, Ira R. and Thompson, Caitlin and Bossarte, Robert M.},
	year = {2017},
	note = {{doi}:10.1002/mpr.1575},
	keywords = {machine learning, assessment/diagnosis, clinical decision support, epidemiology, predictive modeling, suicide/self harm},
	annote = {e1575 IJMPR-May-2017-0053.R1},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/T9TAZCEG/Kessler et al. - 2017 - Developing a practical suicide risk prediction model for targeting high-risk patients in the Veteran.pdf:application/pdf},
}

@book{james_introduction_2021,
	address = {New York},
	edition = {2},
	title = {An introduction to statistical learning: with applications in {R}},
	isbn = {978-1-0716-1417-4 978-1-0716-1418-1},
	shorttitle = {An introduction to statistical learning},
	language = {eng},
	publisher = {Springer},
	author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
	year = {2021},
	annote = {Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility},
	annote = {Previous edition: New York: Springer, 2013 Includes bibliographical references and index},
	file = {Table of Contents PDF:/Users/hanzhangren/Zotero/storage/9GA7N5RU/James et al. - 2021 - An introduction to statistical learning with applications in R.pdf:application/pdf},
}

@book{hastie_elements_2009,
    address = {New York},
	edition = {2},
	title = {The elements of
statistical learning:
data mining, inference, and prediction},
	copyright = {http://www.springer.com/tdm},
	isbn = {978-0-387-84857-0 978-0-387-84858-7},
	urldate = {2025-11-01},
	publisher = {Springer},
	author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
	year = {2009},
}

@article{ginsberg_detecting_2009,
	title = {Detecting influenza epidemics using search engine query data},
	volume = {457},
	copyright = {2009 Macmillan Publishers Limited. All rights reserved},
	issn = {1476-4687},
	abstract = {This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control.},
	language = {en},
	number = {7232},
	urldate = {2025-11-01},
	journal = {Nature},
	publisher = {Nature Publishing Group},
	author = {Ginsberg, Jeremy and Mohebbi, Matthew H. and Patel, Rajan S. and Brammer, Lynnette and Smolinski, Mark S. and Brilliant, Larry},
	month = feb,
	year = {2009},
	note = {{doi}:10.1038/nature07634},
	keywords = {Humanities and Social Sciences, multidisciplinary, Science},
	pages = {1012--1014},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/R5BULM3Y/Ginsberg et al. - 2009 - ginsberg_detecting_2009.pdf:application/pdf},
}

@article{bauer_dyadic_2014,
	series = {Fertility over the {Life} {Course}},
	title = {Dyadic fertility decisions in a life course perspective},
	volume = {21},
	issn = {1569-4909},
	abstract = {This paper focuses on how couples arrive at joint decisions with regard to fertility behaviour. We build upon previous work on decision rules that couples might apply as heuristics in order to arrive at joint action in cases in which partners’ fertility preferences differ. Previous research found either stronger effects of women's desires or symmetrical effects of both spouses’ desires and net benefits associated with (further) children on proceptive behaviour. The latter finding is in line with the notion of household utility maximisation, in which both partners’ preferences enter into a joint utility function with equal weight. On the other hand, some evidence indicates that one partner can exercise a ‘veto’ if he or she anticipates individual utility losses from a further child (due to opportunity costs arising in other life domains). We now enhance previous research by applying a life-course perspective. Our analysis makes use of variation in initial conditions due to previous births: couples decide on fertility in different situations as they find themselves in different life course stages and have had certain experiences. Parity-specific differences affect not only fertility outcomes but also the decision-making process itself. Our findings show that the decision to have a first child is made jointly, and each partner may exercise a veto. On the other hand, women appear to dominate decisions on higher parity births, not per se, but because they are (still) the ones more affected by the concomitant housework.},
	urldate = {2025-11-01},
	journal = {Adv. Life Course Res.},
	author = {Bauer, Gerrit and Kneip, Thorsten},
	month = sep,
	year = {2014},
	note = {{doi}:10.1016/j.alcr.2013.11.003},
	keywords = {Childbearing, Decision rules, Decision-making, Pairfam, Partnership perspective, Utility of children},
	pages = {87--100},
	file = {ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/A6BMQDJ4/S1040260813000427.html:text/html},
}

@misc{centerdata_methodology_2026,
	title = {Methodology},
	abstract = {The Panel. Methodology Sample and recruitment The LISS panel is intended for scientific, policy or socially relevant research. The quality and the coverage of the sample is of prime concern. To establish the LISS panel, a traditional random sample was drawn from the population registers, in collaboration with Statistics Netherlands (Centraal Bureau voor de Statistiek, […]},
	language = {en-US},
	urldate = {2025-10-31},
	author = {{Centerdata}},
	year = {2025},
	note = {{https}://www.lissdata.nl/methodology. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/QDXFUCMQ/methodology.html:text/html},
}

@misc{centerdata_home_2026,
	title = {Home},
	abstract = {Know what’s going on. The LISS panel is the high-quality online research infrastructure in the Netherlands. Representative of the Dutch population. You can collect new data, download existing data or conduct your innovative experiments. The panelThe archiveAbout usNewsFAQ .elementor-701 .elementor-element.elementor-element-7069980 {\textgreater} .elementor-element-populated\{padding:0px 50px 0px 0px;\}.elementor-widget-heading .elementor-heading-title\{\vphantom{\}}font-family:var( –e-global-typography-primary-font-family ), Sans-serif;font-size:var( –e-global-typography-primary-font-size );font-weight:var( –e-global-typography-primary-font-weight );line-height:var( –e-global-typography-primary-line-height );color:var( […]},
	language = {en-US},
	urldate = {2025-10-31},
	author = {{Centerdata}},
	year = {2026},
	note = {{https}://www.lissdata.nl/. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/Y9A5FI8X/www.lissdata.nl.html:text/html},
}

@misc{centerdata_background_2026,
	title = {Background variables},
	abstract = {The Archive. Background Variables General background information is gathered through a separate survey, which the households can update every month. Some of the questions in this survey concern the whole household, while others concern the individual household members. However, all questions of this survey are completed by the household contact person only. The background variables […]},
	language = {en-US},
	urldate = {2025-10-31},
	author = {{Centerdata}},
	year = {2026},
	note = {{https}://www.lissdata.nl/background-variables. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/7D6DLH74/background-variables.html:text/html},
}

@article{thomson_couple_1997,
	title = {Couple childbearing desires, intentions, and births},
	volume = {34},
	issn = {1533-7790},
	abstract = {Using new panel data from the National Surveys of Families and Households, I investigate the effects of wives’ and husbands’ childbearing desires on their spouses’ intentions, and the effects of spouses’ desires and intentions on subsequent births. The results show clearly that husbands’ desires and intentions influence couples’ births, with approximately equal force to that of wives’ desires and intentions. When couples disagreed about wanting a child, each partners’ intentions were shifted toward not having a child; and disagreement in desires or intentions were reflected in birth rates that were lower than average. These patterns were generally not different for couples with more or less traditional gender roles or attitudes.},
	language = {en},
	number = {3},
	urldate = {2025-11-01},
	journal = {Demography},
	author = {Thomson, Elizabeth},
	month = aug,
	year = {1997},
	note = {{doi}:10.2307/3038288},
	keywords = {Childbearing Intention, Desire Family Size, Gender Attitude, Gender Role, Married Couple},
	pages = {343--354},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/9CSKCQZU/Thomson - 1997 - Couple childbearing desires, intentions, and births.pdf:application/pdf},
}

@inproceedings{machado_benchmarking_2022,
	address = {Cham},
	title = {Benchmarking data augmentation techniques for tabular data},
	isbn = {978-3-031-21753-1},
	issn = {1611-3349},
	abstract = {Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class...},
	language = {en},
	urldate = {2025-08-23},
	booktitle = {{Intelligent data engineering and automated learning – IDEAL 2022 23rd international conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, proceedings, part i. Lecture notes in computer science, vol 13756.}},
	publisher = {Springer},
	author = {Machado, Pedro and Fernandes, Bruno and Novais, Paulo},
	editor = {Hujun, Yin and Camacho, David and Tiňo, Peter},
	year = {2022},
	pages = {104--112},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/MLRRWAYU/Machado et al. - 2022 - Benchmarking Data Augmentation Techniques for Tabular Data.pdf:application/pdf},
}

@inproceedings{volkova_overview_2024,
	address = {Cham},
	title = {An overview on data augmentation for machine learning},
	isbn = {978-3-031-55349-3},
	issn = {2367-3389},
	abstract = {The effective utilization of data augmentation stands as a strategic imperative in the domains of industrial enterprises and economics, offering a potent means to enhance the convergence and performance of machine learning models. Data augmentation, as a...},
	language = {en},
	urldate = {2025-08-23},
	booktitle = {Digital and information technologies in economics and management: proceedings of the international scientific and practical conference "{Digital} and information technologies in economics and management" ({DITEM2023}). {Lecture} notes in networks and systems, vol 942.},
	publisher = {Springer},
	author = {Volkova, Svetlana},
	editor = {Gibadullin, Arthur},
	year = {2024},
	pages = {143--154},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/5J3QJQQI/Volkova - 2024 - An Overview on Data Augmentation for Machine Learning.pdf:application/pdf},
}

@article{logeswaran_its_2025,
	title = {It’s about time: why we need to consider temporal drift when developing and implementing clinical prediction models},
	volume = {10},
	issn = {2451-9022},
	shorttitle = {It’s about time},
	language = {English},
	number = {7},
	urldate = {2025-10-31},
	journal = {Biol. Psychiatry: Cogn. Neurosci. Neuroimaging},
	publisher = {Elsevier},
	author = {Logeswaran, Yanakan and Oliver, Dominic},
	month = jul,
	year = {2025},
	note = {{doi}:10.1016/j.bpsc.2025.05.003},
	pages = {677--678},
}

@article{maharana_review_2022,
	series = {International {Conference} on {Intelligent} {Engineering} {Approach}({ICIEA}-2022)},
	title = {A review: data pre-processing and data augmentation techniques},
	volume = {3},
	issn = {2666-285X},
	shorttitle = {A review},
	abstract = {This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. It deals with two significant issues in the pre-processing process (i). issues with data and (ii). Steps to follow to do data analysis with its best approach. As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available. Poor data can primarily affect the accuracy and lead to false prediction, so it is necessary to improve the dataset's quality. So, data pre-processing is the best way to deal with such problems. It makes the knowledge extraction from the data set much easier with cleaning, Integration, transformation, and reduction methods. The issue with Data missing and significant differences in the variety of data always exists as the information is collected through multiple sources and from a real-world application. So, the data augmentation approach generates data for machine learning models. To decrease the dependency on training data and to improve the performance of the machine learning model. This paper discusses flipping, rotating with slight degrees and others to augment the image data and shows how to perform data augmentation methods without distorting the original data.},
	number = {1},
	urldate = {2025-08-23},
	journal = {Glob. Transit. Proc.},
	author = {Maharana, Kiran and Mondal, Surajit and Nemade, Bhushankumar},
	month = jun,
	year = {2022},
	note = {{doi}:10.1016/j.gltp.2022.04.020},
	keywords = {Data augmentation, Data cleaning, Data oversampling, Data pre-processing, Data wraping},
	pages = {91--99},
	file = {ScienceDirect Full Text PDF:/Users/hanzhangren/Zotero/storage/BNFPMADV/Maharana et al. - 2022 - A review data pre-processing and data augmentation techniques.pdf:application/pdf;ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/4MU2ZYT3/S2666285X22000565.html:text/html},
}

@misc{kuhn_boosted_2026,
	title = {Boosted trees via xgboost},
	abstract = {xgboost::xgb.train() creates a series of decision trees forming an
ensemble. Each tree depends on the results of previous trees. All trees in
the ensemble are combined to produce a final prediction.},
	language = {en},
	urldate = {2025-08-29},
	author = {Kuhn, Max and Vaughan, Davis},
	year = {2026},
	note = {{https}://parsnip.tidymodels.org/reference/details\_boost\_tree\_xgboost.html. Accessed 24 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/TYI53L8H/details_boost_tree_xgboost.html:text/html},
}

@article{hughes_mean_1968,
	title = {On the mean accuracy of statistical pattern recognizers},
	volume = {14},
	issn = {1557-9654},
	abstract = {The overall mean recognition probability (mean accuracy) of a pattern classifier is calculated and numerically plotted as a function of the pattern measurement complexity n and design data set sizem. Utilized is the well-known probabilistic model of a two-class, discrete-measurement pattern environment (no Gaussian or statistical independence assumptions are made). The minimum-error recognition rule (Bayes) is used, with the unknown pattern environment probabilities estimated from the data relative frequencies. In calculating the mean accuracy over all such environments, only three parameters remain in the final equation:n, m, and the prior probabilityp\_cof either of the pattern classes. With a fixed design pattern sample, recognition accuracy can first increase as the number of measurements made on a pattern increases, but decay with measurement complexity higher than some optimum value. Graphs of the mean accuracy exhibit both an optimal and a maximum acceptable value ofnfor fixedmandp\_c. A four-place tabulation of the optimumnand maximum mean accuracy values is given for equally likely classes andmranging from2to1000. The penalty exacted for the generality of the analysis is the use of the mean accuracy itself as a recognizer optimality criterion. Namely, one necessarily always has some particular recognition problem at hand whose Bayes accuracy will be higher or lower than the mean over all recognition problems having fixedn, m, andp\_c.},
	number = {1},
	urldate = {2025-08-24},
	journal = {IEEE Trans. Inf. Theory},
	author = {Hughes, Gordon F.},
	month = jan,
	year = {1968},
	note = {{doi}:10.1109/TIT.1968.1054102},
	keywords = {Bayes methods, Complexity theory, Numerical models, Pattern recognition, Probability, Standards, Vectors},
	pages = {55--63},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/XJ5GNMXD/Hughes - 1968 - On the mean accuracy of statistical pattern recognizers.pdf:application/pdf},
}

@article{mumuni_data_2022,
	title = {Data augmentation: a comprehensive survey of modern approaches},
	volume = {16},
	issn = {2590-0056},
	shorttitle = {Data augmentation},
	abstract = {To ensure good performance, modern machine learning models typically require large amounts of quality annotated data. Meanwhile, the data collection and annotation processes are usually performed manually, and consume a lot of time and resources. The quality and representativeness of curated data for a given task is usually dictated by the natural availability of clean data in the particular domain as well as the level of expertise of developers involved. In many real-world application settings it is often not feasible to obtain sufficient training data. Currently, data augmentation is the most effective way of alleviating this problem. The main goal of data augmentation is to increase the volume, quality and diversity of training data. This paper presents an extensive and thorough review of data augmentation methods applicable in computer vision domains. The focus is on more recent and advanced data augmentation techniques. The surveyed methods include deeply learned augmentation strategies as well as feature-level and meta-learning-based data augmentation techniques. Data synthesis approaches based on realistic 3D graphics modeling, neural rendering, and generative adversarial networks are also covered. Different from previous surveys, we cover a more extensive array of modern techniques and applications. We also compare the performance of several state-of-the-art augmentation methods and present a rigorous discussion of the effectiveness of various techniques in different scenarios of use based on performance results on different datasets and tasks.},
	urldate = {2025-08-23},
	journal = {Array},
	author = {Mumuni, Alhassan and Mumuni, Fuseini},
	month = dec,
	year = {2022},
	note = {{doi}:10.1016/j.array.2022.100258},
	keywords = {Computer vision, Generative adversarial network, Machine learning, Meta-learning, Review of data augmentation, Synthetic data},
	file = {ScienceDirect Full Text PDF:/Users/hanzhangren/Zotero/storage/GA4I6WXP/Mumuni and Mumuni - 2022 - Data augmentation a comprehensive survey of modern approaches.pdf:application/pdf;ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/K4W6YLSS/S2590005622000911.html:text/html},
}

@article{chang_predicting_2026,
	title = {Predicting individual’s fertility outcomes: {A} data science approach},
    journal = {},
	author = {Chang, L. Y. and Kandroodi, M. R.},
	year = {2026},
	note = {(working paper)},
}

@article{cantrell_2026_more,
  title = {More Data, Modest Gains: Assessing Limits of Prediction in Multi-Domain Population-Scale Social Data},
  author = {Cantrell, Emily M. and Ren, Hanzhang and Kapoor, Sayash and Verhagen, Mark D. and Narayanan, Arvind and Salganik, Matthew J.},
  journal = {EPJ Data Science},
  year = {2026},
  note = {(manuscript under minor revision)}
}

@article{stock_macroeconomic_2002,
	title = {Macroeconomic forecasting using diffusion indexes},
	volume = {20},
	issn = {0735-0015},
	abstract = {This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-monthahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.},
	number = {2},
	urldate = {2025-08-23},
	journal = {J. Bus. Econ. Stat.},
	author = {Stock, James H. and Watson, Mark W.},
	month = apr,
	year = {2002},
	note = {{doi}:10.1198/073500102317351921},
	keywords = {Factor model, Forecasting, Principal components},
	pages = {147--162},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/JGP27S7G/Stock and Watson - 2002 - Macroeconomic Forecasting Using Diffusion Indexes.pdf:application/pdf},
}

@article{sivak_births_2026,
	title = {A data challenge with rich survey and full-population register data reveals the limits of predicting births},
    journal = {},
	author = {Sivak, Elizaveta and Cantrell, Emily M. and Emery, Thomas and Garcia-Bernardo, Javier and Hafner, Flavio and Karpinska, Kasia and Lüken, Malte and Mendrik, Adrienne and Mulder, Joris and Ren, Hanzhang and Satish, Varun and Verhagen, Mark and Maineri, Angelica and Pankowska, Paulina and Abdel Ghany, Jasmin and Arpino, Bruno and Cassani, Giovanni and Hellstrand, Julia and Ivanova, Katya and Kuikka, Sanni and Macanovic, Ana and Rahal, Charles and Tropf, Felix and Veen, Roland and Walasek, Nicole and van Wijk, Daniël and Wright, Kelsey and Zagheni, Emilio and Abbink, Henry and Aliverti, Emanuele and Amestoy, Matteo and Atav, Tilbe and Barban, Nicola and Billingsley, Sunnee and Booij, Goan and Boucherie, Louis and Broos, Yael and Chang, Li Ya and Chiu, Jamie and Comolli, Chiara Ludovica and Cule, Boris and Fang, Qixiang and Feehan, Dennis and Ganly, Rachel and Gielens, Erwin and Gonzales Martinez, Rolando and Gradassi, Andrea and Guerra-Urzola, Rosember and Guerra-Urzola, Mario and Guerrier, Stéphane and Hassan, Enamul and Haverhoek, Vincent and Hendrickson, Andrew and Howard, Amber and Jin, Yuxuan and Kapoor, Sayash and van Kesteren, Erik-Jan and Klooster, Iris ten and Labussiere, Marie and Liu, Lydia and Liu, Tiffany and Maghout, Adam and Meneghello, Simone and Mohr, Lasse and Mulder, Clara and Newman, Saul and Nisén, Jessica and Norden, Janis and Odgaard, Mikkel and Omenti, Riccardo and Ozdemir, Ozancan and Pao, Christina and Park, Paige and Penta, Gaia and Perdomo, Juan and Pial, Tanzir and Piraccini, Alessio and Querin, Federica and Rao, Ziwei and Rellama, Christian and Remund, Adrien and Richert, Frederieke and van de Rijt, Arnout and Rostami Kandroodi, Mojtaba and Rotman, Stijn and Sage, Lucas and Savcisens, Germans and Schwanitz, Katrin and Skiena, Steven and Spata, Alessandro and Stadtfeld, Yannick and Stroebl, Benedikt and Tedesco, Gaetano and Theelen, Mathilde and Tori, Gianluca and Tun-Mendicuti, Abigail and Tyagi, Rishabh and Vafa, Keyon and Vecchietti, Luiz Felipe and Vecgaile, Linda and Vermeulen, Willem and Victoria Feser, Maria-Pia and Voirol, Lionel and Volker, Thom and Wang, Xinran and Yan, Jiani and Zhao, Xinyi and Zhou, Flora and Zilincikova, Zuzana and Nissim, Malvina and Salganik, Matthew and Stulp, Gert},
	year = {2026},
	note = {(manuscript submitted for
publication)},
	file = {PDF:/Users/hanzhangren/Zotero/storage/ZPVCQVMW/Sivak et al. - 2026 - A data challenge with rich survey and full-population register data reveals the limits of predicting.pdf:application/pdf},
}

@article{van_de_rijt_luck_2026,
	title = {Luck and {predictability} in the {life} {course}},
	volume = {52},
	abstract = {There is an emerging recognition among sociological theorists that luck may play a substantial role in life course achievement. There is also a nascent empirical literature that finds life outcomes to be unpredictable and unexpected life events to be a likely cause. A third literature of causal event studies provides thousands of point estimates of the life course consequences of random events. This review brings these literatures together under a unified framework.},
	language = {en},
	urldate = {2026-07-09},
	journal = {Annu. Rev. Sociol.},
	author = {van de Rijt, Arnout and Bernardi, Fabrizio and Foley, William and Sage, Lucas},
	month = apr,
	year = {2026},
    note = {{doi}:10.1146/annurev-soc-031324-115112},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/6R2QLP4F/annurev-soc-031324-115112.html:text/html},
}

@article{grimmer_machine_2021,
	title = {Machine learning for social science: an agnostic approach},
	volume = {24},
	issn = {1094-2939, 1545-1577},
	shorttitle = {Machine learning for social science},
	abstract = {Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions.},
	language = {en},
	urldate = {2026-07-09},
	journal = {Annu. Rev. Political Sci.},
	author = {Grimmer, Justin and Roberts, Margaret E. and Stewart, Brandon M.},
	month = may,
	year = {2021},
    note = {{doi}:10.1146/annurev-polisci-053119-015921},
	pages = {395--419},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/6M4SJ4YS/annurev-polisci-053119-015921.html:text/html},
}

@article{hofman_integrating_2021,
	title = {Integrating explanation and prediction in computational social science},
	volume = {595},
	copyright = {2021 Springer Nature Limited},
	issn = {1476-4687},
	abstract = {Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.},
	language = {en},
	number = {7866},
	urldate = {2026-07-09},
	journal = {Nature},
	author = {Hofman, Jake M. and Watts, Duncan J. and Athey, Susan and Garip, Filiz and Griffiths, Thomas L. and Kleinberg, Jon and Margetts, Helen and Mullainathan, Sendhil and Salganik, Matthew J. and Vazire, Simine and Vespignani, Alessandro and Yarkoni, Tal},
	month = jul,
	year = {2021},
    note = {{doi}:10.1038/s41586-021-03659-0},
	keywords = {Interdisciplinary studies, Scientific community},
	pages = {181--188},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/6YQTU76U/Hofman et al. - 2021 - Integrating explanation and prediction in computational social science.pdf:application/pdf},
}

@article{verhagen_pragmatists_2022,
	title = {A pragmatist’s guide to using prediction in the social sciences},
	volume = {8},
	issn = {2378-0231},
	abstract = {Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.},
	language = {EN},
	urldate = {2026-07-09},
	journal = {Socius},
	author = {Verhagen, Mark D.},
	month = jan,
	year = {2022},
    note = {{doi}:10.1177/23780231221081702},
	file = {SAGE PDF Full Text:/Users/hanzhangren/Zotero/storage/4B7QDJLC/Verhagen - 2022 - A pragmatist’s guide to using prediction in the social sciences.pdf:application/pdf},
}

@article{kuikka_predictability_2024,
	title = {The (un)predictability of early (un)employment: a machine learning approach},
	volume = {10},
	issn = {2378-0231},
	shorttitle = {The (un)predictability of early (un)employment},
	abstract = {This article integrates scholarship on determinants of labor market transitions with the flexible modeling strategies available through machine learning. Leveraging population registers from Finland, this study analyzes how well we can predict (un)employment and for whom we can (not) predict. The study finds that the predictability of long-term (un)employment improves when using tree-based nonparametric models compared to logistic regression but that predictability varies substantially across outcome groups. None of the models predict very accurately for the unemployed net of baseline likelihood to be unemployed—a group that is well researched and often of interest when designing policies and interventions. Overall accuracy is driven by the employed while incorrectly predicting most of the unemployed. Additionally, the outcomes for individuals with low parental education are overall more difficult to predict than the outcomes for individuals with mid/high parental education, whereas predicting unemployment slightly improves among the low parental education group.},
	language = {EN},
	urldate = {2026-07-09},
	journal = {Socius},
	author = {Kuikka, Sanni},
	month = dec,
	year = {2024},
    note = {{doi}:10.1177/23780231241286655},
	file = {SAGE PDF Full Text:/Users/hanzhangren/Zotero/storage/9GNVFYRP/Kuikka - 2024 - The (un)predictability of early (un)employment a machine learning approach.pdf:application/pdf},
}

@article{zheng_social_2025,
	title = {Social rigidity across and within generations: a predictive approach},
	volume = {54},
	issn = {0049-1241},
	shorttitle = {Social rigidity across and within generations},
	abstract = {How well can individuals’ parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply, social rigidity. Running machine learning models on panel data to predict outcomes that include hourly wage, total income, family income, and occupational status, we find that a large number (around 4,000) of predictors commonly used in the stratification literature improves the prediction of one’s life chances in middle to late adulthood by about 10 percent to 50 percent, compared with a null model that uses a simple mean of the outcome variable. The level of predictability depends on the specific outcome being analyzed, with labor market indicators like wages and occupational prestige being more predictable than broader socioeconomic measures such as overall personal and family income. Grouping a comprehensive list of predictors into four unique sets that cover family background, childhood and adolescence development, early labor market experiences, and early adulthood family formation, we find that including income, employment status, and occupational characteristics at early career significantly improves models’ prediction accuracy for mid-life SES attainment. We also illustrate the application of the predictive models to examine heterogeneity in predictability by race and gender and identify important variables through this data-driven exercise.},
	language = {EN},
	number = {4},
	urldate = {2026-07-09},
	journal = {Sociol. Methods Res.},
	author = {Zheng, Haowen and Cheng, Siwei},
	month = nov,
	year = {2025},
    note = {{doi}:10.1177/00491241251347984},
	pages = {1683--1725},
	file = {SAGE PDF Full Text:/Users/hanzhangren/Zotero/storage/5WR5VA5A/Zheng and Cheng - 2025 - Social rigidity across and within generations a predictive approach.pdf:application/pdf},
}

@article{sivak_combining_2024,
	title = {Combining the strengths of {Dutch} survey and register data in a data challenge to predict fertility ({PreFer})},
	volume = {7},
	issn = {2432-2725},
	abstract = {The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predictive ability on new data. This prevents us from systematically comparing studies, hindering the evaluation and accumulation of knowledge. In this paper, we present two datasets which can be used to study the predictability of fertility outcomes in the Netherlands. One dataset is based on the LISS panel, a longitudinal survey which includes thousands of variables on a wide range of topics, including individual preferences and values. The other is based on the Dutch register data which lacks attitudinal data but includes detailed information about the life courses of millions of Dutch residents. We provide information about the datasets and the samples, and describe the fertility outcome of interest. We also introduce the fertility prediction data challenge PreFer which is based on these datasets and will start in Spring 2024. We outline the ways in which measuring the predictability of fertility outcomes using these datasets and combining their strengths in the data challenge can advance our understanding of fertility behaviour and computational social science. We further provide details for participants on how to take part in the data challenge.},
	language = {en},
	number = {2},
	urldate = {2026-07-24},
	journal = {J. Comput. Soc. Sci.},
	author = {Sivak, Elizaveta and Pankowska, Paulina and Mendrik, Adriënne and Emery, Tom and Garcia-Bernardo, Javier and Höcük, Seyit and Karpinska, Kasia and Maineri, Angelica and Mulder, Joris and Nissim, Malvina and Stulp, Gert},
	month = oct,
	year = {2024},
	note = {{doi}:10.1007/s42001-024-00275-6},
	keywords = {Benchmark, Data challenge, Fertility, Out-of-sample prediction, Register data, Survey data},
	pages = {1403--1431},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/BZG95UPR/Sivak et al. - 2024 - Combining the strengths of Dutch survey and register data in a data challenge to predict fertility (.pdf:application/pdf},
}


@misc{statistics_netherlands_inflation_2022,
	title = {Inflation rate up to 14.5 percent in {September}},
	abstract = {The consumer price index (CPI) was 14.5 percent higher in September than in the same month last year.},
	language = {en-GB},
	urldate = {2026-07-25},
	author = {{Statistics Netherlands}},
	month = oct,
	year = {2022},
	note = {{https}://www.cbs.nl/en-gb/news/2022/40/inflation-rate-up-to-14-5-percent-in-september. Accessed 25 July 2026},
	file = {Snapshot:/Users/hanzhangren/Zotero/storage/DBGLQZZI/inflation-rate-up-to-14-5-percent-in-september.html:text/html},
}


@article{allison_discrete_1982,
	title = {Discrete-time methods for the analysis of event histories},
	volume = {13},
	issn = {0081-1750},
	urldate = {2026-07-26},
	journal = {Sociol. Methodol.},
	author = {Allison, Paul D.},
	year = {1982},
	note = {{doi}:10.2307/270718},
	pages = {61--98},
	file = {JSTOR Full Text PDF:/Users/hanzhangren/Zotero/storage/54YUQDNZ/Allison - 1982 - Discrete-time methods for the analysis of event histories.pdf:application/pdf},
}

@book{singer_applied_2003,
	address = {New York},
	title = {Applied longitudinal data analysis: modeling change and event occurrence},
	isbn = {978-0-19-515296-8 978-0-19-976072-5},
	shorttitle = {Applied longitudinal data analysis},
	abstract = {Cover -- Contents -- PART I -- 1 A Framework for Investigating Change over Time -- 1.1 When Might You Study Change over Time? -- 1.2 Distinguishing Between Two Types of Questions about Change -- 1.3 Three Important Features of a Study of Change -- 2 Exploring Longitudinal Data on Change -- 2.1 Creating a Longitudinal Data Set -- 2.2 Descriptive Analysis of Individual Change over Time -- 2.3 Exploring Differences in Change across People -- 2.4 Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design -- 3 Introducing the Multilevel Model for Change -- 3.1 What Is the Purpose of the Multilevel Model for Change? -- 3.2 The Level-1 Submodel for Individual Change -- 3.3 The Level-2 Submodel for Systematic Interindividual Differences in Change -- 3.4 Fitting the Multilevel Model for Change to Data -- 3.5 Examining Estimated Fixed Effects -- 3.6 Examining Estimated Variance Components -- 4 Doing Data Analysis with the Multilevel Model for Change -- 4.1 Example: Changes in Adolescent Alcohol Use -- 4.2 The Composite Specification of the Multilevel Model for Change -- 4.3 Methods of Estimation, Revisited -- 4.4 First Steps: Fitting Two Unconditional Multilevel Models for Change -- 4.5 Practical Data Analytic Strategies for Model Building -- 4.6 Comparing Models Using Deviance Statistics -- 4.7 Using Wald Statistics to Test Composite Hypotheses About Fixed Effects -- 4.8 Evaluating the Tenability of a Model's Assumptions -- 4.9 Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters -- 5 Treating TIME More Flexibly -- 5.1 Variably Spaced Measurement Occasions -- 5.2 Varying Numbers of Measurement Occasions -- 5.3 Time-Varying Predictors -- 5.4 Recentering the Effect of TIME -- 6 Modeling Discontinuous and Nonlinear Change -- 6.1 Discontinuous Individual Change},
	language = {eng},
	publisher = {Oxford University Press},
	author = {Singer, Judith D. and Willett, John B.},
	year = {2003},
	annote = {Description based on publisher supplied metadata and other sources},
}


@article{roberts_cross_2017,
	title = {Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure},
	volume = {40},
	issn = {1600-0587},
	abstract = {Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.},
	language = {en},
	number = {8},
	urldate = {2026-07-26},
	journal = {Ecography},
	author = {Roberts, David R. and Bahn, Volker and Ciuti, Simone and Boyce, Mark S. and Elith, Jane and Guillera-Arroita, Gurutzeta and Hauenstein, Severin and Lahoz-Monfort, José J. and Schröder, Boris and Thuiller, Wilfried and Warton, David I. and Wintle, Brendan A. and Hartig, Florian and Dormann, Carsten F.},
	year = {2017},
	note = {{doi}:10.1111/ecog.02881},
	pages = {913--929},
	file = {Full Text PDF:/Users/hanzhangren/Zotero/storage/TTSP3AZT/Roberts et al. - 2017 - Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure.pdf:application/pdf;Snapshot:/Users/hanzhangren/Zotero/storage/9DYYIGXA/ecog.html:text/html},
}

@article{bergmeir_use_2012,
	series = {Data {Mining} for {Software} {Trustworthiness}},
	title = {On the use of cross-validation for time series predictor evaluation},
	volume = {191},
	issn = {0020-0255},
	abstract = {In time series predictor evaluation, we observe that with respect to the model selection procedure there is a gap between evaluation of traditional forecasting procedures, on the one hand, and evaluation of machine learning techniques on the other hand. In traditional forecasting, it is common practice to reserve a part from the end of each time series for testing, and to use the rest of the series for training. Thus it is not made full use of the data, but theoretical problems with respect to temporal evolutionary effects and dependencies within the data as well as practical problems regarding missing values are eliminated. On the other hand, when evaluating machine learning and other regression methods used for time series forecasting, often cross-validation is used for evaluation, paying little attention to the fact that those theoretical problems invalidate the fundamental assumptions of cross-validation. To close this gap and examine the consequences of different model selection procedures in practice, we have developed a rigorous and extensive empirical study. Six different model selection procedures, based on (i) cross-validation and (ii) evaluation using the series’ last part, are used to assess the performance of four machine learning and other regression techniques on synthetic and real-world time series. No practical consequences of the theoretical flaws were found during our study, but the use of cross-validation techniques led to a more robust model selection. To make use of the “best of both worlds”, we suggest that the use of a blocked form of cross-validation for time series evaluation became the standard procedure, thus using all available information and circumventing the theoretical problems.},
	urldate = {2026-07-27},
	journal = {Inf. Sci.},
	author = {Bergmeir, Christoph and Benítez, José M.},
	month = may,
	year = {2012},
	note = {{doi}:10.1016/j.ins.2011.12.028},
	keywords = {Cross-validation, Error measures, Machine learning, Predictor evaluation, Regression, Time series},
	pages = {192--213},
	file = {ScienceDirect Full Text PDF:/Users/hanzhangren/Zotero/storage/GKYEUV6B/Bergmeir and Benítez - 2012 - On the use of cross-validation for time series predictor evaluation.pdf:application/pdf;ScienceDirect Snapshot:/Users/hanzhangren/Zotero/storage/ELNUNTTR/S0020025511006773.html:text/html},
}
