Further Reading

A curated reading list organized by topic. This list will grow as the workshop preparation continues — check back for updates.

Foundations of Causal Inference

Textbooks & Overviews

  • Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5th ed.). The Guilford Press. — Covers covariance-based SEM, Pearl’s nonparametric SEM, and composite SEM. Accessible introduction to causal reasoning from an SEM perspective.

The Interventionist Account of Causation

The philosophical foundations of the manipulationist/interventionist framework underlying graphical causal inference.

Why Causal Thinking? — Advocacy & Motivation

  • Hernán, M. A. (2018). The C-word: Scientific euphemisms do not improve causal inference from observational data. American Journal of Public Health, 108(5), 616–619. https://doi.org/10.2105/AJPH.2018.304337
  • Ahern, J. (2018). Start with the “C-word,” follow the roadmap for causal inference. American Journal of Public Health, 108(5), 621. https://doi.org/10.2105/AJPH.2018.304358
  • Grosz, M. P., Rohrer, J. M., & Thoemmes, F. (2020). The taboo against explicit causal inference in nonexperimental psychology. Perspectives on Psychological Science, 15(5), 1243–1255. https://doi.org/10.1177/1745691620921521
  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629
  • Rohrer, J. M. (2024). Causal inference for psychologists who think that causal inference is not for them. Social and Personality Psychology Compass, 18(3), e12948. https://doi.org/10.1111/spc3.12948

Case Study: Income and Happiness

A contemporary example of how causal assumptions matter — even in high-profile adversarial collaborations published in PNAS.

  • Killingsworth, M. A., Kahneman, D., & Mellers, B. (2023). Income and emotional well-being: A conflict resolved. Proceedings of the National Academy of Sciences, 120(10). https://doi.org/10.1073/pnas.2208661120
  • Rohrer, J. M. & Wenz, S. E. (2024). Inappropriate causal assumptions underlie Killingsworth, Kahneman, and Mellers’ conclusions. Proceedings of the National Academy of Sciences, 121(46). https://doi.org/10.1073/pnas.2313712121
  • Killingsworth, M. A., Kahneman, D., & Mellers, B. (2024). Reply to Rohrer and Wenz and Arslan: The association between income and emotional well-being. Proceedings of the National Academy of Sciences, 121(46). https://doi.org/10.1073/pnas.2322160121

Structural Causal Models & DAGs

  • Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
  • Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley.
  • Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of Causal Analysis for Social Research (pp. 245–273). Springer. https://doi.org/10.1007/978-94-007-6094-3_13
  • Textor, J., van der Zander, B., Gilthorpe, M. S., Liskiewicz, M., & Ellison, G. T. H. (2016). Robust causal inference using directed acyclic graphs: The R package dagitty. International Journal of Epidemiology, 45(6), 1887–1894. https://doi.org/10.1093/ije/dyw341
  • Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146. https://doi.org/10.1214/09-SS057

Paradoxes

Simpson’s paradox and Lord’s paradox both arise when conditioning on a variable reverses or changes an association. Tu et al. (2008) show they are manifestations of the same underlying phenomenon — the reversal paradox. Understanding these paradoxes through the lens of causal graphs is central to the workshop.

Simpson’s Paradox

Lord’s Paradox & Change Scores

  • Tennant, P. W. G., Arnold, K. F., Ellison, G. T. H., & Gilthorpe, M. S. (2022). Analyses of ‘change scores’ do not estimate causal effects in observational data. International Journal of Epidemiology, 51(5), 1604–1615. https://academic.oup.com/ije/article-abstract/51/5/1604/6294759
  • Popham, F. (2022). Definition yes, analysis no: a comment on ‘analyses of “change scores” do not estimate causal effects in observational data’. International Journal of Epidemiology, 51(5), 1691–1692. https://doi.org/10.1093/ije/dyab202
  • Tennant, P. W. G., Tomova, G. D., Murray, E. J., Arnold, K. F., Fox, M. P., & Gilthorpe, M. S. (2023). Lord’s ‘paradox’ explained: the 50-year warning on the use of ‘change scores’ in observational data. arXiv:2302.01822. http://arxiv.org/abs/2302.01822

The Reversal Paradox — Unifying Simpson’s and Lord’s

  • Tu, Y.-K., Gunnell, D., & Gilthorpe, M. S. (2008). Simpson’s paradox, Lord’s paradox, and suppression effects are the same phenomenon — the reversal paradox. Emerging Themes in Epidemiology, 5(1), 2. https://doi.org/10.1186/1742-7622-5-2

Identification & Adjustment

Backdoor Criterion & Covariate Selection

The Do-Calculus

  • Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688. https://doi.org/10.1093/biomet/82.4.669
  • Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
  • Shpitser, I., & Pearl, J. (2006). Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06) (pp. 1219–1226). https://cdn.aaai.org/AAAI/2006/AAAI06-191.pdf

Mediation Analysis

Causal approaches to mediation, direct and indirect effects, and the graphical perspective on mediation.

Critiques of Traditional Mediation

Causal Mediation Analysis

  • Pearl, J. (2001). Direct and indirect effects. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (pp. 411–420). Morgan Kaufmann. https://ftp.cs.ucla.edu/pub/stat_ser/R273-U.pdf
  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334. https://doi.org/10.1037/a0020761
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. https://doi.org/10.1214/10-STS321
  • VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press.
  • Nguyen, T. Q., Schmid, I., & Stuart, E. A. (2021). Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn. Psychological Methods, 26(2), 255–271. https://doi.org/10.1037/met0000299
  • Fritz, M. S. & MacKinnon, D. P. (2008). A graphical representation of the mediated effect. Behavior Research Methods, 40(1), 55–60. https://doi.org/10.3758/brm.40.1.55

Causal Moderation & Interaction

Causal Graphs for Missing Data

Using DAGs to reason about missing data mechanisms and their implications for identification and estimation.

  • Daniel, R. M., Kenward, M. G., Cousens, S. N., & De Stavola, B. L. (2012). Using causal diagrams to guide analysis in missing data problems. Statistical Methods in Medical Research, 21(3), 243–256. https://doi.org/10.1177/0962280210394469
  • Thoemmes, F., & Mohan, K. (2015). Graphical representation of missing data problems. Structural Equation Modeling: A Multidisciplinary Journal, 22(4), 631–642. https://doi.org/10.1080/10705511.2014.937378
  • Mohan, K., & Pearl, J. (2021). Graphical models for processing missing data. Journal of the American Statistical Association, 116(534), 1023–1037. https://doi.org/10.1080/01621459.2021.1874961
  • Moreno-Betancur, M., Lee, K. J., Leacy, F. P., White, I. R., Simpson, J. A., & Carlin, J. B. (2018). Canonical causal diagrams to guide the treatment of missing data in epidemiologic studies. American Journal of Epidemiology, 187(12), 2705–2715. https://doi.org/10.1093/aje/kwy173

Multilevel & Clustered Data

  • McNeish, D., & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24(1), 20–35. https://doi.org/10.1037/met0000182
  • McNeish, D. (2023). A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models. Psychological Methods. https://doi.org/10.1037/met0000620
  • Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101(475), 901–910. https://doi.org/10.1198/016214506000000447
  • Hazlett, C., & Wainstein, L. (2022). Understanding, choosing, and unifying multilevel and fixed effect approaches. Political Analysis, 30(1), 46–65. https://doi.org/10.1017/pan.2020.41
  • Sobel, M. E. (2006). What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. Journal of the American Statistical Association, 101(476), 1398–1407. https://doi.org/10.1198/016214506000000636

Longitudinal & Panel Data

Dynamic Models & Cross-Lagged Panel Models

  • Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
  • Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82(5), 879–894. https://doi.org/10.1037/a0035297
  • Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5), 637–657. https://doi.org/10.1037/met0000210
  • Mund, M., & Nestler, S. (2019). Beyond the cross-lagged panel model: Next-generation statistical tools for analyzing interdependencies across the life course. Advances in Life Course Research, 41, 100249. https://doi.org/10.1016/j.alcr.2018.10.002
  • Mulder, J. D., & Hamaker, E. L. (2021). Three extensions of the random intercept cross-lagged panel model. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 638–648. https://doi.org/10.1080/10705511.2020.1784738
  • Rohrer, J. M., & Murayama, K. (2023). These are not the effects you are looking for: Causality and the within-/between-persons distinction in longitudinal data analysis. Advances in Methods and Practices in Psychological Science, 6(1). https://doi.org/10.1177/25152459221140842
  • Quintana, R. (2021). Thinking within-persons: Using unit fixed-effects models to describe causal mechanisms. Methods in Psychology, 5, 100076. https://doi.org/10.1016/j.metip.2021.100076
  • Helske, J., & Tikka, S. (2024). Estimating causal effects from panel data with dynamic multivariate panel models. Advances in Life Course Research, 60, 100617. https://doi.org/10.1016/j.alcr.2024.100617

Causal Discovery & LLMs

  • Kıcıman, E., Ness, R., Sharma, A., & Tan, C. (2024). Causal reasoning and large language models: Opening a new frontier for causality. Transactions on Machine Learning Research. https://arxiv.org/abs/2305.00050
  • Darvariu, V.-A., Hailes, S., & Musolesi, M. (2024). Large language models are effective priors for causal graph discovery. arXiv:2405.13551. https://arxiv.org/abs/2405.13551
  • Survey: LLMs for causal discovery — current landscape and future directions (2025). arXiv:2402.11068. https://arxiv.org/abs/2402.11068

Tools & Software

  • DAGitty — Browser-based tool for drawing and analyzing causal graphs
  • Barrett, M., D’Agostino McGowan, L., & Gerke, T. (2024). Causal Inference in R. https://www.r-causal.org — Free online book; applied, code-focused companion to DAG-based causal inference.
  • D’Agostino McGowan, L., Gerke, T., & Barrett, M. (2023). Causal inference is not just a statistics problem. Journal of Statistics and Data Science Education, 31(3), 291–300. https://doi.org/10.1080/26939169.2023.2276446 — Introduces the “causal quartet”: four datasets with identical associations but different causal structures.
  • R packages from the r-causal project: ggdag (DAG visualization), quartets (causal quartet datasets), tipr (tipping-point sensitivity analysis), halfmoon (propensity score diagnostics).