References

The list below is the consolidated bibliography for the book, auto-generated from references.bib. Canonical references for the potential-outcomes framework and the modern toolkit follow Imbens and Rubin (2015) and Hernán and Robins (2020).

Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425.
Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2010. “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association 105 (490): 493–505.
———. 2015. “Comparative Politics and the Synthetic Control Method.” American Journal of Political Science 59 (2): 495–510.
Abadie, Alberto, and Javier Gardeazabal. 2003. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review 93 (1): 113–32.
Abadie, Alberto, and Guido W. Imbens. 2006. “Large Sample Properties of Matching Estimators for Average Treatment Effects.” Econometrica 74 (1): 235–67.
Adão, Rodrigo, Michal Kolesár, and Eduardo Morales. 2019. “Shift-Share Designs: Theory and Inference.” Quarterly Journal of Economics 134 (4): 1949–2010.
Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91 (434): 444–55.
Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. 2021. “Synthetic Difference-in-Differences.” American Economic Review 111 (12): 4088–118.
Athey, Susan, and Guido W. Imbens. 2017. “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives 31 (2): 3–32.
Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. “Generalized Random Forests.” Annals of Statistics 47 (2): 1148–78.
Athey, Susan, and Stefan Wager. 2021. “Policy Learning with Observational Data.” Econometrica 89 (1): 133–61.
Autor, David H., David Dorn, and Gordon H. Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” American Economic Review 103 (6): 2121–68.
Bang, Heejung, and James M. Robins. 2005. “Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61 (4): 962–73.
Baron, Reuben M., and David A. Kenny. 1986. “The Moderator-Mediator Variable Distinction in Social Psychological Research.” Journal of Personality and Social Psychology 51 (6): 1173–82.
Bartik, Timothy J. 1991. Who Benefits from State and Local Economic Development Policies? W.E. Upjohn Institute.
Bezanson, Jeff, Alan Edelman, Stefan Karpinski, and Viral B. Shah. 2017. “Julia: A Fresh Approach to Numerical Computing.” SIAM Review 59 (1): 65–98.
Bhattacharya, Rohit, Razieh Nabi, and Ilya Shpitser. 2022. “Semiparametric Inference for Causal Effects in Graphical Models with Hidden Variables.” Journal of Machine Learning Research 23: 1–76.
Bickel, Peter J., Chris A. J. Klaassen, Ya’acov Ritov, and Jon A. Wellner. 1993. Efficient and Adaptive Estimation for Semiparametric Models. Johns Hopkins University Press.
Bollen, Kenneth A. 1989. Structural Equations with Latent Variables. Wiley.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel. 2022. “Quasi-Experimental Shift-Share Research Designs.” Review of Economic Studies 89 (1): 181–213.
Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2024. “Revisiting Event Study Designs: Robust and Efficient Estimation.” Review of Economic Studies 91 (6): 3253–85.
Botosaru, Irene, and Laura Liu. 2025. “Time-Varying Heterogeneous Treatment Effects in Event Studies.” arXiv Preprint arXiv:2509.13698. https://arxiv.org/abs/2509.13698.
———. 2026. “Event Studies with Feedback.” AEA Papers and Proceedings 116: 70–74. https://doi.org/10.1257/pandp.20261110.
Callaway, Brantly, and Tong Li. 2019. “Quantile Treatment Effects in Difference in Differences Models with Panel Data.” Quantitative Economics 10 (4): 1579–1618.
Callaway, Brantly, and Pedro H. C. Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230.
Calonico, Sebastian, Matias D. Cattaneo, and Rocı́o Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.” Econometrica 82 (6): 2295–2326.
Chaisemartin, Clément de, and Xavier D’Haultfœuille. 2020. “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review 110 (9): 2964–96.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1): C1–68.
Chernozhukov, Victor, Mert Demirer, Esther Duflo, and Iván Fernández-Val. 2018. “Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments.” NBER Working Paper 24678.
Chernozhukov, Victor, Iván Fernández-Val, and Blaise Melly. 2013. “Inference on Counterfactual Distributions.” Econometrica 81 (6): 2205–68.
Chernozhukov, Victor, and Christian Hansen. 2005. “An IV Model of Quantile Treatment Effects.” Econometrica 73 (1): 245–61.
Chickering, David Maxwell. 2002. “Optimal Structure Identification with Greedy Search.” Journal of Machine Learning Research 3: 507–54.
Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. BART: Bayesian Additive Regression Trees.” Annals of Applied Statistics 4 (1): 266–98.
Cinelli, Carlos, and Chad Hazlett. 2020. “Making Sense of Sensitivity: Extending Omitted Variable Bias.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (1): 39–67.
Colombo, Diego, Marloes H. Maathuis, Markus Kalisch, and Thomas S. Richardson. 2012. “Learning High-Dimensional Directed Acyclic Graphs with Latent and Selection Variables.” Annals of Statistics 40 (1): 294–321.
Cox, David R. 1972. “Regression Models and Life-Tables.” Journal of the Royal Statistical Society: Series B (Methodological) 34 (2): 187–202.
Cui, Yifan, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, and Ruoqing Zhu. 2023. “Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 85 (2): 179–211.
Daniel, Rhian M., Bianca L. De Stavola, Simon N. Cousens, and Stijn Vansteelandt. 2015. “Causal Mediation Analysis with Multiple Mediators.” Biometrics 71 (1): 1–14.
Dehejia, Rajeev H., and Sadek Wahba. 1999. “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association 94 (448): 1053–62.
Dı́az, Iván, and Mark J. van der Laan. 2018. “Stochastic Treatment Regimes.” In Targeted Learning in Data Science, 219–32. Springer.
Dı́az, Iván, Nicholas Williams, Katherine L. Hoffman, and Edward J. Schenck. 2023. “Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies.” Journal of the American Statistical Association 118 (542): 846–57.
Firpo, Sergio. 2007. “Efficient Semiparametric Estimation of Quantile Treatment Effects.” Econometrica 75 (1): 259–76.
Firpo, Sergio, and Vitor Possebom. 2018. “Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets.” Journal of Causal Inference 6 (2).
Fisher, Ronald A. 1935. The Design of Experiments. Oliver; Boyd.
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift. 2020. “Bartik Instruments: What, When, Why, and How.” American Economic Review 110 (8): 2586–2624.
Goodman-Bacon, Andrew. 2021. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics 225 (2): 254–77.
Hahn, P. Richard, Carlos M. Carvalho, David Puelz, and Jingyu He. 2018. “Regularization and Confounding in Linear Regression for Treatment Effect Estimation.” Bayesian Analysis 13 (1): 163–82.
Hahn, P. Richard, Jared S. Murray, and Carlos M. Carvalho. 2020. “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects.” Bayesian Analysis 15 (3): 965–1056.
Hainmueller, Jens. 2012. “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20 (1): 25–46.
Heckman, James J., and Edward Vytlacil. 2005. “Structural Equations, Treatment Effects, and Econometric Policy Evaluation.” Econometrica 73 (3): 669–738.
Hernán, Miguel A. 2010. “The Hazards of Hazard Ratios.” Epidemiology 21 (1): 13–15.
Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC.
Hill, Jennifer L. 2011. “Bayesian Nonparametric Modeling for Causal Inference.” Journal of Computational and Graphical Statistics 20 (1): 217–40.
Hirano, Keisuke, and Guido W. Imbens. 2004. “The Propensity Score with Continuous Treatments.” In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73–84. Wiley.
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.” Political Analysis 15 (3): 199–236.
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.
Huling, Jared D., and Simon Mak. 2024. “Energy Balancing of Covariate Distributions.” Journal of Causal Inference 12 (1).
Iacus, Stefano M., Gary King, and Giuseppe Porro. 2012. “Causal Inference Without Balance Checking: Coarsened Exact Matching.” Political Analysis 20 (1): 1–24.
Imai, Kosuke, and Marc Ratkovic. 2014. “Covariate Balancing Propensity Score.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1): 243–63.
Imbens, Guido W. 2000. “The Role of the Propensity Score in Estimating Dose-Response Functions.” Biometrika 87 (3): 706–10.
———. 2004. “Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review.” Review of Economics and Statistics 86 (1): 4–29.
Imbens, Guido W., and Joshua D. Angrist. 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica 62 (2): 467–75.
Imbens, Guido W., and Thomas Lemieux. 2008. “Regression Discontinuity Designs: A Guide to Practice.” Journal of Econometrics 142 (2): 615–35.
Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
Kang, Joseph D. Y., and Joseph L. Schafer. 2007. “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.” Statistical Science 22 (4): 523–39.
Kennedy, Edward H. 2020. “Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects.” arXiv Preprint arXiv:2004.14497.
———. 2022. “Semiparametric Doubly Robust Targeted Double Machine Learning: A Review.” arXiv Preprint arXiv:2203.06469.
Kennedy, Edward H., Zongming Ma, Matthew D. McHugh, and Dylan S. Small. 2017. “Non-Parametric Methods for Doubly Robust Estimation of Continuous Treatment Effects.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79 (4): 1229–45.
Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. 2019. “Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning.” Proceedings of the National Academy of Sciences 116 (10): 4156–65.
Laan, Mark J. van der, and Susan Gruber. 2012. “Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions.” The International Journal of Biostatistics 8 (1).
Laan, Mark J. van der, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer.
Laan, Mark J. van der, and Daniel Rubin. 2006. “Targeted Maximum Likelihood Learning.” The International Journal of Biostatistics 2 (1).
Lee, David S. 2008. “Randomized Experiments from Non-Random Selection in U.S. House Elections.” Journal of Econometrics 142 (2): 675–97.
———. 2009. “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies 76 (3): 1071–1102.
Lee, Yu-Chin, and Jeffrey M. Wooldridge. 2025. “Distributional Difference-in-Differences via Engression.” Working Paper.
Manski, Charles F. 1990. “Nonparametric Bounds on Treatment Effects.” American Economic Review Papers and Proceedings 80 (2): 319–23.
McCandless, Lawrence C., Paul Gustafson, and Adrian Levy. 2007. “Bayesian Sensitivity Analysis for Unmeasured Confounding in Observational Studies.” Statistics in Medicine 26 (11): 2331–47.
Mullahy, John. 1997. “Instrumental-Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior.” Review of Economics and Statistics 79 (4): 586–93.
Neyman, Jerzy. 1990. “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.” Statistical Science 5 (4): 465–72.
Nie, Xinkun, and Stefan Wager. 2021. “Quasi-Oracle Estimation of Heterogeneous Treatment Effects.” Biometrika 108 (2): 299–319.
Pearl, Judea. 1995. “Causal Diagrams for Empirical Research.” Biometrika 82 (4): 669–88.
———. 2001. “Direct and Indirect Effects.” In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI), 411–20.
———. 2009. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press.
Petersen, Maya, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, and Mark van der Laan. 2014. “Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.” Journal of Causal Inference 2 (2): 147–85.
Rho, Saeyoung, Cyrus Illick, Samhitha Narasipura, Alberto Abadie, Daniel Hsu, and Vishal Misra. 2026. “Time-Aware Synthetic Control.” arXiv Preprint arXiv:2601.03099. https://arxiv.org/abs/2601.03099.
Robins, James M. 1986. “A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period—Application to Control of the Healthy Worker Survivor Effect.” Mathematical Modelling 7 (9–12): 1393–1512.
Robins, James M., and Dianne M. Finkelstein. 2000. “Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted Log-Rank Tests.” Biometrics 56 (3): 779–88.
Robins, James M., Miguel Á. Hernán, and Babette Brumback. 2000. “Marginal Structural Models and Causal Inference in Epidemiology.” Epidemiology 11 (5): 550–60.
Robins, James M., Andrea Rotnitzky, and Lue Ping Zhao. 1994. “Estimation of Regression Coefficients When Some Regressors Are Not Always Observed.” Journal of the American Statistical Association 89 (427): 846–66.
Rosenbaum, Paul R. 2002. Observational Studies. 2nd ed. Springer.
Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55.
Rosseel, Yves. 2012. “Lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software 48 (2): 1–36.
Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–44.
Rotnitzky, Andrea, and James M. Robins. 2005. “Inverse Probability Weighted Estimation in Survival Analysis.” In Encyclopedia of Biostatistics. Wiley.
Royston, Patrick, and Mahesh K. B. Parmar. 2013. “Restricted Mean Survival Time: An Alternative to the Hazard Ratio for the Design and Analysis of Randomized Trials with a Time-to-Event Outcome.” BMC Medical Research Methodology 13: 152.
Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5): 688–701.
Shen, Xinwei, and Nicolai Meinshausen. 2024. “Engression: Extrapolation Through the Lens of Distributional Regression.” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Shimizu, Shohei, Patrik O. Hoyer, Aapo Hyvärinen, and Antti Kerminen. 2006. “A Linear Non-Gaussian Acyclic Model for Causal Discovery.” Journal of Machine Learning Research 7: 2003–30.
Shpitser, Ilya, and Judea Pearl. 2006. “Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models.” In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), 1219–26.
Spirtes, Peter, and Clark Glymour. 1991. “An Algorithm for Fast Recovery of Sparse Causal Graphs.” Social Science Computer Review 9 (1): 62–72.
Spirtes, Peter, Clark Glymour, and Richard Scheines. 2000. Causation, Prediction, and Search. 2nd ed. MIT Press.
Spirtes, Peter, Christopher Meek, and Thomas Richardson. 1995. “Causal Inference in the Presence of Latent Variables and Selection Bias.” In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI), 499–506.
Stuart, Elizabeth A. 2010. “Matching Methods for Causal Inference: A Review and a Look Forward.” Statistical Science 25 (1): 1–21.
Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–99.
Textor, Johannes, Benito van der Zander, Mark S. Gilthorpe, Maciej Liśkiewicz, and George T. H. Ellison. 2016. “Robust Causal Inference Using Directed Acyclic Graphs: The R Package dagitty.” International Journal of Epidemiology 45 (6): 1887–94.
Thistlethwaite, Donald L., and Donald T. Campbell. 1960. “Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment.” Journal of Educational Psychology 51 (6): 309–17.
Tikka, Santtu, Antti Hyttinen, and Juha Karvanen. 2019. “Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach.” Journal of Statistical Software 99 (5).
Uno, Hajime, Brian Claggett, Lu Tian, Eisuke Inoue, Paul Gallo, Toshio Miyata, Deborah Schrag, et al. 2014. “Moving Beyond the Hazard Ratio in Quantifying the Between-Group Difference in Survival Analysis.” Journal of Clinical Oncology 32 (22): 2380–85.
VanderWeele, Tyler J., and Peng Ding. 2017. “Sensitivity Analysis in Observational Research: Introducing the E-Value.” Annals of Internal Medicine 167 (4): 268–74.
VanderWeele, Tyler J., and Stijn Vansteelandt. 2009. “Conceptual Issues Concerning Mediation, Interventions and Composition.” Statistics and Its Interface 2 (4): 457–68.
Vansteelandt, Stijn, and Rhian M. Daniel. 2017. “Interventional Effects for Mediation Analysis with Multiple Mediators.” Epidemiology 28 (2): 258–65.
Wager, Stefan, and Susan Athey. 2018. “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests.” Journal of the American Statistical Association 113 (523): 1228–42.
Windmeijer, Frank A. G., and João M. C. Santos Silva. 1997. “Endogeneity in Count Data Models: An Application to Demand for Health Care.” Journal of Applied Econometrics 12 (3): 281–94.
Wooldridge, Jeffrey M. 2021. “Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators.” SSRN Working Paper.
Xu, Ruonan. 2023. “Difference-in-Differences with Interference.” arXiv Preprint arXiv:2306.12003. https://arxiv.org/abs/2306.12003.
———. 2026. “Dynamic Difference-in-Differences with Interference.” AEA Papers and Proceedings 116: 58–63. https://doi.org/10.1257/pandp.20261108.