References

Published

December 27, 2025

Modified

December 28, 2025

Keywords

A/B Testing, Causal Inference, Causal Time Series Analysis, Data Science, Difference-in-Differences, Directed Acyclic Graphs, Econometrics, Impact Evaluation, Instrumental Variables, Heterogeneous Treatment Effects, Potential Outcomes, Power Analysis, Sample Size Calculation, Python and R Programming, Randomized Experiments, Regression Discontinuity, Treatment Effects

Get the new chapters straight to your inbox. Leave your address here.
Found an error or have a suggestion? Submit feedback here.
Follow the book’s page for new content and corrections, or follow the author.

Abadie, Alberto. 2005. “Semiparametric Difference-in-Differences Estimators.” Review of Economic Studies 72 (1): 1–19. https://doi.org/10.1111/0034-6527.00321.
Ahrens, Achim, Victor Chernozhukov, Christian Hansen, Damian Kozbur, Mark Schaffer, and Thomas Wiemann. 2025. “An Introduction to Double/Debiased Machine Learning.” https://doi.org/10.48550/arXiv.2504.08324.
Angrist, Joshua D., Carol Gao, Peter Hull, and Robert W. Yeh. 2025. “Instrumental Variables in Randomized Trials.” NEJM Evidence 4 (4): EVIDctw2400204. https://doi.org/10.1056/EVIDctw2400204.
Angrist, Joshua D., and Peter Hull. 2023. “Instrumental Variables Methods Reconcile Intention-to-Screen Effects Across Pragmatic Cancer Screening Trials.” Proceedings of the National Academy of Sciences 120 (51): e2311556120. https://doi.org/10.1073/pnas.2311556120.
Angrist, Joshua D., and Alan B. Krueger. 2001. “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments.” Journal of Economic Perspectives 15 (4): 69–85. https://doi.org/10.1257/jep.15.4.69.
Angrist, Joshua D., and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.
———. 2015. Mastering ’Metrics: The Path from Cause to Effect. Princeton, NJ: Princeton University Press.
Angrist, Joshua D., and Miikka Rokkanen. 2015. “Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away from the Cutoff.” Journal of the American Statistical Association 110 (512): 1331–44. https://doi.org/10.1080/01621459.2015.1012259.
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. https://doi.org/10.1257/aer.20190159.
Aronow, Peter M., and Cyrus Samii. 2017. “Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment.” Annals of Applied Statistics 11 (4): 1912–47. https://doi.org/10.1214/16-AOAS1005.
Ashenfelter, Orley. 1978. “Estimating the Effect of Training Programs on Earnings.” Review of Economics and Statistics 60 (1): 47–57. https://doi.org/10.2307/1924332.
Athey, Susan, and Guido Imbens. 2016. “Recursive Partitioning for Heterogeneous Causal Effects.” Proceedings of the National Academy of Sciences 113 (27): 7353–60. https://doi.org/10.1073/pnas.1510489113.
Athey, Susan, and Guido W. Imbens. 2017. “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives 31 (2): 3–32. https://doi.org/10.1257/jep.31.2.3.
———. 2019. “Machine Learning Methods That Economists Should Know About.” Annual Review of Economics 11: 685–725. https://doi.org/10.1146/annurev-economics-080217-053433.
Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. “Generalized Random Forests.” Annals of Statistics 47 (2): 1148–78. https://doi.org/10.1214/18-AOS1709.
Athey, Susan, and Stefan Wager. 2021. “Policy Learning with Observational Data.” Econometrica 89 (1): 133–61. https://doi.org/10.3982/ECTA15732.
Austin, Peter C. 2011. “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46 (3): 399–424. https://doi.org/10.1080/00273171.2011.568786.
Baird, Sarah, J. Aislinn Bohren, Craig McIntosh, and Berk Özler. 2018. “Optimal Design of Experiments in the Presence of Interference.” Review of Economics and Statistics 100 (5): 844–60. https://doi.org/10.1162/rest_a_00716.
Baker, Andrew, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H. C. Sant’Anna. 2026. “Difference-in-Differences Designs: A Practitioner’s Guide.” Journal of Economic Literature 64 (2): 498–557. https://doi.org/10.1257/jel.20251650.
Barreca, Alan I., Jason M. Lindo, and Glen R. Waddell. 2016. “Heaping-Induced Bias in Regression-Discontinuity Designs.” Economic Inquiry 54 (1): 268–93. https://doi.org/10.1111/ecin.12225.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics 119 (1): 249–75. https://doi.org/10.1162/003355304772839588.
Blake, Thomas, and Dominic Coey. 2014. “Why Marketplace Experimentation Is Harder Than It Seems: The Role of Test-Control Interference.” In Proceedings of the 15th ACM Conference on Economics and Computation, 567–82. https://doi.org/10.1145/2600057.2602837.
Boegershausen, Johannes, Yann Cornil, Shangwen Yi, and David J. Hardisty. 2025. “On the Persistent Mischaracterization of Google and Facebook a/b Tests: How to Conduct and Report Online Platform Studies.” International Journal of Research in Marketing 42 (3): 886–903. https://doi.org/10.1016/j.ijresmar.2024.12.004.
Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2024. “Revisiting Event-Study Designs: Robust and Efficient Estimation.” Review of Economic Studies 91 (6): 3253–85. https://doi.org/10.1093/restud/rdae007.
Brand, Jennie E., Xiang Zhou, and Yu Xie. 2023. “Recent Developments in Causal Inference and Machine Learning.” Annual Review of Sociology 49: 81–110. https://doi.org/10.1146/annurev-soc-030420-015345.
Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9 (1): 247–74. https://doi.org/10.1214/14-AOAS788.
Caliendo, Marco, and Sabine Kopeinig. 2008. “Some Practical Guidance for the Implementation of Propensity Score Matching.” Journal of Economic Surveys 22 (1): 31–72. https://doi.org/10.1111/j.1467-6419.2007.00527.x.
Callaway, Brantly, and Pedro H. C. Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001.
Calonico, Sebastian, Matias D. Cattaneo, and Rocio Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.” Econometrica 82 (6): 2295–2326. https://doi.org/10.3982/ECTA11757.
Cameron, A. Colin, and Douglas L. Miller. 2015. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources 50 (2): 317–72. https://doi.org/10.3368/jhr.50.2.317.
Card, David, David S. Lee, Zhuan Pei, and Andrea Weber. 2015. “Inference on Causal Effects in a Generalized Regression Kink Design.” Econometrica 83 (6): 2453–83. https://doi.org/10.3982/ECTA11224.
Casella, George, and Roger L. Berger. 2002. Statistical Inference. 2nd ed. Pacific Grove, CA: Duxbury/Thomson Learning.
Cattaneo, Matias D., Nicolás Idrobo, and Rocío Titiunik. 2020. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge: Cambridge University Press.
———. 2024. A Practical Introduction to Regression Discontinuity Designs: Extensions. Elements in Quantitative and Computational Methods for the Social Sciences. Cambridge: Cambridge University Press.
Cattaneo, Matias D., Michael Jansson, and Xinwei Ma. 2020. “Simple Local Polynomial Density Estimators.” Journal of the American Statistical Association 115 (531): 1449–55. https://doi.org/10.1080/01621459.2019.1635480.
Cattaneo, Matias D., Luke Keele, Rocío Titiunik, and Gonzalo Vazquez-Bare. 2016. “Interpreting Regression Discontinuity Designs with Multiple Cutoffs.” Journal of Politics 78 (4): 1229–48. https://doi.org/10.1086/686802.
Cattaneo, Matias D., and Rocío Titiunik. 2022. “Regression Discontinuity Designs.” Annual Review of Economics 14: 821–51. https://doi.org/10.1146/annurev-economics-051520-021409.
Cattaneo, Matias D., Rocío Titiunik, and Ruiqi Yu. 2025. “Estimation and Inference in Boundary Discontinuity Designs.” Working Paper.
Celli, Viviana. 2022. “Causal Mediation Analysis in Economics: Objectives, Assumptions, Models.” Journal of Economic Surveys 36 (1): 214–34. https://doi.org/10.1111/joes.12452.
Chabé-Ferret, Sylvain. 2025. “Statistical Tools for Causal Inference.” 2025. https://chabefer.github.io/STCI/.
Chaisemartin, Clément de, Diego Ciccia, Felix Knau, Mélitine Malézieux, Doulo Sow, David Arboleda, Romain Angotti, et al. 2025. “Using Did_multiplegt_dyn to Estimate Event-Study Effects in Complex Designs: Overview, and Four Examples Based on Real Datasets.” https://doi.org/10.48550/arXiv.2510.19426.
Chaisemartin, Clément de, and Xavier D’Haultfœuille. 2020. “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review 110 (9): 2964–96. https://doi.org/10.1257/aer.20181169.
———. 2025. Causal Inference with Differences-in-Differences: Credible Answers to Hard Questions. Princeton, NJ: Princeton University Press. https://ssrn.com/abstract=4487202.
Chang, Neng-Chieh. 2020. “Double/Debiased Machine Learning for Difference-in-Differences Models.” Econometrics Journal 23 (2): 177–91. https://doi.org/10.1093/ectj/utaa001.
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. https://doi.org/10.1111/ectj.12097.
Chernozhukov, Victor, Mert Demirer, Esther Duflo, and Iván Fernández-Val. 2023. “Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India.” arXiv:1712.04802. https://arxiv.org/abs/1712.04802.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. 2024. Applied Causal Inference Powered by ML and AI. Online. https://causalml-book.org/.
Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98. https://doi.org/10.1214/09-AOAS285.
Chiu, Albert, Xingchen Lan, Ziyi Liu, and Yiqing Xu. 2026. “Causal Panel Analysis Under Parallel Trends: Lessons from a Large Reanalysis Study.” American Political Science Review 120 (1): 245–66. https://doi.org/10.1017/S0003055425000243.
Cinelli, Carlos, Jeremy Ferwerda, and Chad Hazlett. 2024. “Sensemakr: Sensitivity Analysis Tools for OLS in R and Stata.” Observational Studies 10 (2): 93–127. https://doi.org/10.1353/obs.2024.a946583.
Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2024. “A Crash Course in Good and Bad Controls.” Sociological Methods & Research 53 (3): 1071–1104. https://doi.org/10.1177/00491241221099552.
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. https://doi.org/10.1111/rssb.12348.
Cunningham, Scott. 2021. “Causal Inference: The Mixtape.” 2021. https://mixtape.scunning.com/.
Deng, Alex, Ulf Knoblich, and Jiannan Lu. 2018. “Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 233–42. ACM. https://doi.org/10.1145/3219819.3219919.
Dong, Yingying, and Arthur Lewbel. 2015. “Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models.” Review of Economics and Statistics 97 (5): 1081–92. https://doi.org/10.1162/REST_a_00510.
Dudík, Miroslav, John Langford, and Lihong Li. 2011. “Doubly Robust Policy Evaluation and Learning.” In Proceedings of the 28th International Conference on Machine Learning, 1097–1104.
Duflo, Esther, Rachel Glennerster, and Michael Kremer. 2006. “Using Randomization in Development Economics Research: A Toolkit.” NBER Technical Working Paper 333. National Bureau of Economic Research. https://doi.org/10.3386/t0333.
Eggers, Andrew C., Guadalupe Tuñón, and Allan Dafoe. 2024. “Placebo Tests for Causal Inference.” American Journal of Political Science 68 (3): 1106–21. https://doi.org/10.1111/ajps.12818.
Facure, Matheus. 2020. “Causal Inference for the Brave and True.” 2020. https://matheusfacure.github.io/python-causality-handbook/landing-page.html.
———. 2023. Causal Inference in Python. O’Reilly.
Fieller, E. C. 1954. “Some Problems in Interval Estimation.” Journal of the Royal Statistical Society: Series B (Methodological) 16 (2): 175–85. https://doi.org/10.1111/j.2517-6161.1954.tb00159.x.
Ganong, Peter, and Simon Jäger. 2018. “A Permutation Test for the Regression Kink Design.” Journal of the American Statistical Association 113 (522): 494–504. https://doi.org/10.1080/01621459.2017.1328356.
Gelman, Andrew. 2018. “You Need 16 Times the Sample Size to Estimate an Interaction Than to Estimate a Main Effect.” Statistical Modeling, Causal Inference, and Social Science (blog). https://statmodeling.stat.columbia.edu/2018/03/15/need16/.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. 3rd ed. CRC Press.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and Other Stories. Cambridge University Press.
Georgiev, Georgi Z. 2019. Statistical Methods in Online a/b Testing: Statistics for Data-Driven Business Decisions and Risk Management in e-Commerce. Independently published.
Glymour, Clark, Kun Zhang, and Peter Spirtes. 2019. “Review of Causal Discovery Methods Based on Graphical Models.” Frontiers in Genetics 10: 524. https://doi.org/10.3389/fgene.2019.00524.
Goodman-Bacon, Andrew. 2021. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics 225 (2): 254–77. https://doi.org/10.1016/j.jeconom.2021.03.014.
Gordon, Brett R., Robert Moakler, and Florian Zettelmeyer. 2023. “Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement.” Marketing Science 42 (4): 768–93. https://doi.org/10.1287/mksc.2022.1413.
Gutierrez, Pierre, and Jean-Yves Gérardy. 2017. “Causal Inference and Uplift Modeling: A Review of the Literature.” In Proceedings of Machine Learning Research, 67:1–13. http://proceedings.mlr.press/v67/gutierrez17a.html.
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. https://doi.org/10.1214/19-BA1195.
Hartman, Erin, and F. Daniel Hidalgo. 2018. “An Equivalence Approach to Balance and Placebo Tests.” American Journal of Political Science 62 (4): 1000–1013. https://doi.org/10.1111/ajps.12387.
Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC. https://miguelhernan.org/whatifbook.
Hill, Jennifer L. 2011. “Bayesian Nonparametric Modeling for Causal Inference.” Journal of Computational and Graphical Statistics 20 (1): 217–40. https://doi.org/10.1198/jcgs.2010.08162.
Hirano, Keisuke, and Guido W. Imbens. 2001. “Estimation of Causal Effects Using Propensity Score Weighting: An Application to Data on Right Heart Catheterization.” Health Services and Outcomes Research Methodology 2: 259–78. https://doi.org/10.1023/A:1020371312283.
Horngren, Charles T., Srikant M. Datar, and Madhav V. Rajan. 2012. Cost Accounting: A Managerial Emphasis. 14th Global. Pearson.
Hoyer, Patrik O., Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf. 2008. “Nonlinear Causal Discovery with Additive Noise Models.” In Advances in Neural Information Processing Systems 21 (NIPS 2008), 689–96.
Huang, Yimin, and Marco Valtorta. 2006. “Pearl’s Calculus of Intervention Is Complete.” In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI-06). AUAI Press.
Huber, Martin. 2023. Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in r. MIT Press. https://mitpress.mit.edu/9780262545914/causal-analysis/.
———. 2024. “An Introduction to Causal Discovery.” Swiss Journal of Economics and Statistics 160: 14. https://doi.org/10.1186/s41937-024-00131-4.
Hudgens, Michael G., and M. Elizabeth Halloran. 2008. “Toward Causal Inference with Interference.” Journal of the American Statistical Association 103 (482): 832–42. https://doi.org/10.1198/016214508000000292.
Huntington-Klein, Nick. 2021. “Robustness Tests: What, Why, and How.” 2021. https://nickchk.com/robustness.html.
———. 2023. “The Effect: An Introduction to Research Design and Causality.” 2023. https://theeffectbook.net/.
Hyndman, Rob J, and George Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. Melbourne, Australia: OTexts. https://otexts.com/fpp3/.
Hyndman, Rob J, George Athanasopoulos, A Garza, C Challu, M Mergenthaler, and K G Olivares. 2025. Forecasting: Principles and Practice, the Pythonic Way. Melbourne, Australia: OTexts. https://otexts.com/fpppy.
Ibragimov, Rustam, and Ulrich K. Müller. 2016. “Inference with Few Heterogeneous Clusters.” Review of Economics and Statistics 98 (1): 83–96. https://doi.org/10.1162/REST_a_00545.
Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. 2011. “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.” American Political Science Review 105 (4): 765–89. https://doi.org/10.1017/S0003055411000414.
Imai, Kosuke, Luke Keele, and Teppei Yamamoto. 2010. “Identification, Inference and Sensitivity Analysis for Causal Mediation Effects.” Statistical Science 25 (1): 51–71. https://doi.org/10.1214/10-STS321.
Imbens, Guido W, and Donald B Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge: Cambridge University Press.
Imbens, Guido, and Andrew Gelman. 2019. “Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs.” Journal of Business & Economic Statistics 37 (3): 447–56. https://doi.org/10.1080/07350015.2017.1366909.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in r. Springer Texts in Statistics. Springer.
James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. 2023. An Introduction to Statistical Learning with Applications in Python. Springer Texts in Statistics. Springer. https://doi.org/10.1007/978-3-031-38747-0.
Jung, Yonghan, Jin Tian, and Elias Bareinboim. 2021. “Double Machine Learning Density Estimation for Local Treatment Effects with Instruments.” In Advances in Neural Information Processing Systems, edited by M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan, 34:21821–33. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2021/file/b61a560ed1b918340a0ddd00e08c990e-Paper.pdf.
Kahneman, Daniel. 2011. Thinking, Fast and Slow. New York: Farrar, Straus; Giroux.
Keele, Luke, and Rocío Titiunik. 2015. “Geographic Boundaries as Regression Discontinuities.” Political Analysis 23 (1): 127–55. https://doi.org/10.1093/pan/mpu014.
———. 2018. “Geographic Natural Experiments with Interference: The Effect of All-Mail Voting on Turnout in Colorado.” CESifo Economic Studies 64 (2): 127–49. https://doi.org/10.1093/cesifo/ify004.
Kennedy, Edward H. 2023. “Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects.” Electronic Journal of Statistics 17 (2). https://doi.org/10.1214/23-EJS2157.
Kohavi, Ron, and Roger Longbotham. 2011. “Unexpected Results in Online Controlled Experiments.” ACM SIGKDD Explorations Newsletter 12 (2): 31–35. https://doi.org/10.1145/1964897.1964905.
Kohavi, Ron, Diane Tang, and Ya Xu. 2020. Trustworthy Online Controlled Experiments: A Practical Guide to a/b Testing. Cambridge University Press.
Kruschke, John K. 2014. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. 2nd ed. Academic Press.
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. https://arxiv.org/abs/1706.03461.
LaPierre, Nathan. 2024. PySensemakr: Sensemakr for Python.” Python package version 0.0.8, https://github.com/nlapier2/PySensemakr.
Lawlor, Debbie A., Kate Tilling, and George Davey Smith. 2016. “Triangulation in Aetiological Epidemiology.” International Journal of Epidemiology 45 (6): 1866–86. https://doi.org/10.1093/ije/dyw314.
Lee, David S, Justin McCrary, Marcelo J Moreira, and Jack Porter. 2022. “Valid t-Ratio Inference for IV.” American Economic Review 112 (10): 3260–90. https://doi.org/10.1257/aer.20211063.
Li, Fan, Peng Ding, and Fabrizia Mealli. 2023. “Bayesian Causal Inference: A Critical Review.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381 (2247): 20220153. https://doi.org/10.1098/rsta.2022.0153.
Lipsitch, Marc, Eric Tchetgen Tchetgen, and Ted Cohen. 2010. “Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies.” Epidemiology 21 (3): 383–88. https://doi.org/10.1097/EDE.0b013e3181d61eeb.
List, John A., Ian Muir, and Gregory Sun. 2025. “Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments.” Econometric Reviews 44 (1): 2–40. https://doi.org/10.1080/07474938.2024.2373446.
List, John A., Azeem M. Shaikh, and Yang Xu. 2019. “Multiple Hypothesis Testing in Experimental Economics.” Experimental Economics 22 (4): 773–93. https://doi.org/10.1007/s10683-018-09597-5.
MacKinnon, James G., and Matthew D. Webb. 2018. “The Wild Bootstrap for Few (Treated) Clusters.” Econometrics Journal 21 (2): 114–35. https://doi.org/10.1111/ectj.12107.
———. 2020. “Randomization Inference for Difference-in-Differences with Few Treated Clusters.” Journal of Econometrics 218 (2): 435–50. https://doi.org/10.1016/j.jeconom.2020.04.024.
MailerLite. 2025. “Email Marketing Benchmarks by Industry and Region for 2026.” MailerLite. 2025. https://www.mailerlite.com/blog/compare-your-email-performance-metrics-industry-benchmarks.
Masten, Matthew A., and Alexandre Poirier. 2018. “Identification of Treatment Effects Under Conditional Partial Independence.” Econometrica 86 (1): 317–51. https://doi.org/10.3982/ECTA14481.
———. 2020. “Inference on Breakdown Frontiers.” Quantitative Economics 11 (1): 41–111. https://doi.org/10.3982/QE1288.
Masten, Matthew A., Alexandre Poirier, and Linqi Zhang. 2024. “Assessing Sensitivity to Unconfoundedness: Estimation and Inference.” Journal of Business & Economic Statistics 42 (1): 1–13. https://doi.org/10.1080/07350015.2023.2183212.
McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd ed. CRC Press.
Menchetti, Fiammetta, Fabrizio Cipollini, and Fabrizia Mealli. 2023. “Combining Counterfactual Outcomes and ARIMA Models for Policy Evaluation.” Econometrics Journal 26 (1): 1–24. https://doi.org/10.1093/ectj/utac024.
Moffitt, Robert A. 2001. “Policy Interventions, Low-Level Equilibria, and Social Interactions.” In Social Dynamics, edited by Steven N. Durlauf and H. Peyton Young, 45–82. Cambridge, MA: MIT Press.
Molak, Aleksander. 2023. Causal Inference and Discovery in Python. Packt Publishing.
Morey, Richard D., Rink Hoekstra, Jeffrey N. Rouder, Michael D. Lee, and Eric-Jan Wagenmakers. 2016. “The Fallacy of Placing Confidence in Confidence Intervals.” Psychonomic Bulletin & Review 23 (1): 103–23. https://doi.org/10.3758/s13423-015-0947-8.
Neumayer, Eric, and Thomas Plümper. 2017. Robustness Tests for Quantitative Research. Methodological Tools in the Social Sciences. Cambridge, United Kingdom: Cambridge University Press.
Noack, Claudia, and Christoph Rothe. 2023. “Donut Regression Discontinuity Designs.” https://doi.org/10.48550/arXiv.2308.14464.
Orellana, Sebastián, Leandro Magga, Paolo Gorgi, Hyeokmoon Kweon, and Felipe Bahamonde. 2024. “Estimating Causal Effects of Customer Satisfaction on Downstream Metrics in a Multi-Queue Contact Center.” https://doi.org/10.48550/arXiv.2412.04860.
Oster, Emily. 2019. “Unobservable Selection and Coefficient Stability: Theory and Evidence.” Journal of Business & Economic Statistics 37 (2): 187–204. https://doi.org/10.1080/07350015.2016.1227711.
Pearl, Judea. 2001. “Direct and Indirect Effects.” In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI 2001), 411–20. Morgan Kaufmann.
———. 2009a. “Causal Inference in Statistics: An Overview.” Statistics Surveys 3: 96–146. https://doi.org/10.1214/09-SS057.
———. 2009b. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press.
———. 2010. “The Foundations of Causal Inference.” Sociological Methodology 40 (1): 75–149. https://doi.org/10.1111/j.1467-9531.2010.01228.x.
———. 2019. “The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM 62 (3): 54–60. https://doi.org/10.1145/3241036.
Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. Wiley.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.
Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. 2017. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press.
Popper, Karl R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge & Kegan Paul.
Rachitsky, Lenny. 2023. “What Is a Good Free-to-Paid Conversion Rate?” Lenny’s Newsletter. 2023. https://www.lennysnewsletter.com/p/what-is-a-good-free-to-paid-conversion.
Raiffa, Howard, and Robert Schlaifer. 1961. Applied Statistical Decision Theory. Boston: Division of Research, Graduate School of Business Administration, Harvard University.
Rambachan, Ashesh, and Jonathan Roth. 2023. “A More Credible Approach to Parallel Trends.” Review of Economic Studies 90 (5): 2555–91. https://doi.org/10.1093/restud/rdad018.
Robins, James M., and Sander Greenland. 1992. “Identifiability and Exchangeability for Direct and Indirect Effects.” Epidemiology 3 (2): 143–55. https://doi.org/10.1097/00001648-199203000-00013.
Robins, James M., Miguel Ángel Hernán, and Babette Brumback. 2000. “Marginal Structural Models and Causal Inference in Epidemiology.” Epidemiology 11 (5): 550–60. https://doi.org/10.1097/00001648-200009000-00011.
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. https://doi.org/10.1016/j.jeconom.2023.03.008.
Rüttenauer, Tobias, and Ozan Aksoy. 2024. “When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators.” https://doi.org/10.48550/arXiv.2402.09928.
Sant’Anna, Pedro H. C., and Jun Zhao. 2020. “Doubly Robust Difference-in-Differences Estimators.” Journal of Econometrics 219 (1): 101–22. https://doi.org/10.1016/j.jeconom.2020.06.003.
Semenova, Vira, and Victor Chernozhukov. 2021. “Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions.” The Econometrics Journal 24 (2): 264–89. https://doi.org/10.1093/ectj/utaa027.
Sharma, Amit, and Emre Kiciman. 2020. “DoWhy: An End-to-End Library for Causal Inference.” https://doi.org/10.48550/arXiv.2011.04216.
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 Twenty-First National Conference on Artificial Intelligence (AAAI-06), 1219–26. AAAI Press.
Simonsohn, Uri, Joseph P. Simmons, and Leif D. Nelson. 2020. “Specification Curve Analysis.” Nature Human Behaviour 4: 1208–14. https://doi.org/10.1038/s41562-020-0912-z.
Sobel, Michael 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.
Spirtes, Peter, Clark Glymour, and Richard Scheines. 2000. Causation, Prediction, and Search. 2nd ed. MIT Press.
Steegen, Sara, Francis Tuerlinckx, Andrew Gelman, and Wolf Vanpaemel. 2016. “Increasing Transparency Through a Multiverse Analysis.” Perspectives on Psychological Science 11 (5): 702–12. https://doi.org/10.1177/1745691616658637.
Stock, James H., and Motohiro Yogo. 2005. “Testing for Weak Instruments in Linear IV Regression.” In Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, edited by Donald W. K. Andrews and James H. Stock, 80–108. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511614491.006.
Stuart, Elizabeth A. 2010. “Matching Methods for Causal Inference: A Review and a Look Forward.” Statistical Science 25 (1): 1–21. https://doi.org/10.1214/09-STS313.
Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–99. https://doi.org/10.1016/j.jeconom.2020.09.006.
Sverdrup, Erik, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, and Stefan Wager. 2020. “Policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees.” Journal of Open Source Software 5 (50): 2232. https://doi.org/10.21105/joss.02232.
———. 2026. “Policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees. R Package Version 1.2.4, Reference Manual.” https://grf-labs.github.io/policytree/reference/hybrid_policy_tree.html.
Taleb, Nassim Nicholas. 2007. The Black Swan: The Impact of the Highly Improbable. New York: Random House.
Thomke, Stefan H. 2020. Experimentation Works: The Surprising Power of Business Experiments. Harvard Business Press.
Tingley, Dustin, Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. 2014. “Mediation: R Package for Causal Mediation Analysis.” Journal of Statistical Software 59 (5): 1–38. https://doi.org/10.18637/jss.v059.i05.
Tran, Alan, Aurélien Bibaut, and Nathan Kallus. 2024. “Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments.” In Proceedings of the 41st International Conference on Machine Learning (ICML). https://arxiv.org/abs/2311.08527.
VanderWeele, Tyler J. 2016. “Mediation Analysis: A Practitioner’s Guide.” Annual Review of Public Health 37: 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402.
VanderWeele, Tyler J., and Peng Ding. 2017. “Sensitivity Analysis in Observational Research: Introducing the e-Value.” Annals of Internal Medicine 167 (4): 268–74. https://doi.org/10.7326/M16-2607.
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. https://doi.org/10.1080/01621459.2017.1319839.
Wong, Vivian C., Peter M. Steiner, and Thomas D. Cook. 2013. “Analyzing Regression-Discontinuity Designs with Multiple Assignment Variables: A Comparative Study of Four Estimation Methods.” Journal of Educational and Behavioral Statistics 38 (2): 107–41. https://doi.org/10.3102/1076998611432172.
Wooldridge, Jeffrey M. 2015. Introductory Econometrics: A Modern Approach. 6th ed. Cengage Learning.
Xu, Muxi, Kun Hu, Sudeep Das, and Bruce Wang. 2025. “Causal Machine Learning for Promotions: Industry Evidence and Applications.” In Proceedings of the KDD 2025 Workshop on Causal Inference and Machine Learning in Practice. Toronto, ON, Canada: ACM.
Yadlowsky, Steve, Scott Fleming, Nigam Shah, Emma Brunskill, and Stefan Wager. 2023. “Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects.” arXiv:2111.07966. https://arxiv.org/abs/2111.07966.
Zhou, Zhengyuan, Susan Athey, and Stefan Wager. 2023. “Offline Multi-Action Policy Learning: Generalization and Optimization.” Operations Research 71 (1): 148–83. https://doi.org/10.1287/opre.2022.2271.