Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure

References

• Angrist JD, Pischke J-S (2008) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Balke A, Pearl J (1997) Bounds on treatment effects from studies with imperfect compliance. J. Amer. Statist. Assoc. 92(439):1171–1176.Crossref, Google Scholar
• Bapna R, Ramaprasad J, Shmueli G, Umyarov A (2016) One-way mirrors in online dating: A randomized field experiment. Management Sci. 62(11):3100–3122.Link, Google Scholar
• Bareinboim E, Pearl J (2016) Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. USA 113(27):7345–7352.Crossref, Google Scholar
• Bellemare MF, Bloem JR, Wexler N (2020) The paper of how: Estimating treatment effects using the front-door criterion. Working paper, University of Minnesota, Minneapolis. Accessed July 1, 2020, http://marcfbellemare.com/wordpress/wp-content/uploads/2020/06/BellemareBloemWexlerFDCJune2020.pdf.Google Scholar
• Berkson J (1946) Limitations of the application of fourfold table analysis to hospital data. Biometrics Bull. 2(3):47–53.Crossref, Google Scholar
• Brito C, Pearl J (2002) Generalized instrumental variables. Darwiche A, Friedman N, eds. Proc. 18th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers Inc., San Francisco), pp. 85–93.Google Scholar
• Clarke B (2016) Why these tech companies keep running thousands of failed experiments. Fast Company (September 21), Accessed July 1, 2020, http://www.fastcompany.com/3063846/why-these-tech-companies-keep-running-thousands-of-failed.Google Scholar
• Cunningham S (2019) Causal inference: The mixtape (version 1.7). Accessed July 1, 2020, http://scunning.com/cunningham_mixtape.pdf.Google Scholar
• Dawid P (2010) Seeing and doing: The Pearlian synthesis. Dechter R, Geffner H, Halpern JY, eds. Heuristics, Probability and Causality: A Tribute to Judea Pearl (College Publications, London), 309–326.Google Scholar
• Deaton A, Cartwright N (2018) Understanding and misunderstanding randomized controlled trials. Soc. Sci. Medicine 210:2–21.Crossref, Google Scholar
• Frangakis CE, Rubin DB (2002) Principal stratification in causal inference. Biometrics 58(1):21–29.Crossref, Google Scholar
• Franzese RJ, Kam C (2009) Modeling and Interpreting Interactive Hypotheses in Regression Analysis (University of Michigan Press, Ann Arbor).Google Scholar
• Gerber AS, Green DP (2012) Field Experiments: Design, Analysis, and Interpretation (WW Norton, New York).Google Scholar
• Gilbert PB, Hudgens MG, Wolfson J (2011) Commentary on “Principal Stratification—A Goal or a Tool?” by Judea Pearl. Internat. J. Biostatistics 7(1):1–15.Crossref, Google Scholar
• Glymour MM, Weuve J, Berkman LF, Kawachi I, Robins JM (2005) When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Amer. J. Epidemiology 162(3):267–278.Crossref, Google Scholar
• Glynn AN, Kashin K (2017) Front-door vs. back-door adjustment with unmeasured confounding: Bias formulas for front-door and hybrid adjustments with application to a job training program. J. Amer. Statist. Assoc. 113(523):1040–1049.Crossref, Google Scholar
• Goel V (2014) Facebook tinkers with users’ emotions in news feed experiment, stirring outcry. New York Times (June 29), Accessed July 1, 2020, http://www.nytimes.com/2014/06/30/technology/facebook-tinkers-with-users-emotions-in-news-feed-experiment-stirring-outcry.html.Google Scholar
• Greene WH (2003) Econometric Analysis, 5th ed. (Prentice Hall, Upper Saddle River, NJ).Google Scholar
• Greenland S, Pearl J, Robins J (1999) Causal diagrams for epidemiologic research. Epidemiology 10(1):37–48.Crossref, Google Scholar
• Halpern JY, Pearl J (2005) Causes and explanations: A structural-model approach. Part II: Explanations. British J. Philos. Sci. 56(4):889–911.Crossref, Google Scholar
• Hayashi F (2000) Econometrics (Princeton University Press, Princeton, NJ).Google Scholar
• Heckman JJ, Pinto R (2015) Econometric mediation analyses: Identifying the sources of treatment effects from experimentally estimated production technologies with unmeasured and mismeasured inputs. Econometric Rev. 34(1–2):6–31.Crossref, Google Scholar
• Hernán MA, VanderWeele TJ (2011) Compound treatments and transportability of causal inference. Epidemiology 22(3):368.Crossref, Google Scholar
• Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA (2002) Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Amer. J. Epidemiology 155(2):176–184.Crossref, Google Scholar
• Horowitz JL (2019) Bootstrap methods in econometrics. Annual Rev. Econom. 11:193–224.Crossref, Google Scholar
• Imai K, Keele L, Tingley D (2010) A general approach to causal mediation analysis. Psych. Methods 15(4):309–334.Crossref, Google Scholar
• Imai K, Keele L, Tingley D, Yamamoto T (2011) Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. Amer. Political Sci. Rev. 105(4):765–789.Crossref, Google Scholar
• Imbens G (2014) Rejoinder. Statist. Sci. 29(3):375–379.Crossref, Google Scholar
• Imbens G (2019) Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. NBER Working Paper No. 26104, National Bureau of Economic Research Working Paper, Cambridge, MA.Crossref, Google Scholar
• Imbens GW, Rubin DB (2015) Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, New York).Crossref, Google Scholar
• Kahan BC, Jairath V, Doré CJ, Morris TP (2014) The risks and rewards of covariate adjustment in randomized trials: An assessment of 12 outcomes from 8 studies. Trials 15(1):139.Crossref, Google Scholar
• Mayeda ER, Banack HR, Bibbins-Domingo K, Al Hazzouri AZ, Marden JR, Whitmer RA, Glymour MM (2018) Can survival bias explain the age attenuation of racial inequalities in stroke incidence?: A simulation study. Epidemiology 29(4):525–532.Crossref, Google Scholar
• Montgomery JM, Nyhan B, Torres M (2018) How conditioning on posttreatment variables can ruin your experiment and what to do about it. Amer. J. Political Sci. 62(3):760–775.Crossref, Google Scholar
• Morgan SL, Winship C (2015) Counterfactuals and Causal Inference, 2nd ed. (Cambridge University Press, New York).Google Scholar
• Mutz DC, Pemantle R, Pham P (2018) The perils of balance testing in experimental design: Messy analyses of clean data. Amer. Statist. 73(1):1–11.Google Scholar
• Pearl J (1995) Causal diagrams for empirical research. Biometrika 82(4):669–688.Crossref, Google Scholar
• Pearl J (2009) Causality: Models, Reasoning, and Inference, 2nd ed. (Cambridge University Press, New York).Crossref, Google Scholar
• Pearl J (2014) Interpretation and identification of causal mediation. Psych. Methods 19(4):459.Crossref, Google Scholar
• Pearl J (2015) Trygve Haavelmo and the emergence of causal calculus. Econometric Theory 31(1):152–179.Crossref, Google Scholar
• Pearl J, Bareinboim E (2014) External validity: From do-calculus to transportability across populations. Statist. Sci. 29(4):579–595.Crossref, Google Scholar
• Pearl J, Mackenzie D (2018) The Book of Why: The New Science of Cause and Effect (Basic Books, New York).Google Scholar
• Pearl J, Glymour M, Jewell NP (2016) Causal Inference in Statistics: A Primer (John Wiley & Sons, West Sussex, UK).Google Scholar
• Pocock SJ, Assmann SE, Enos LE, Kasten LE (2002) Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: Current practice and problems. Statist. Medicine 21(19):2917–2930.Crossref, Google Scholar
• Preacher KJ (2015) Advances in mediation analysis: A survey and synthesis of new developments. Annual Rev. Psych. 66:825–852.Crossref, Google Scholar
• Raab GM, Day S, Sales J (2000) How to select covariates to include in the analysis of a clinical trial. Controlled Clinical Trials 21(4):330–342.Crossref, Google Scholar
• Shadish W, Cook TD, Campbell DT (2002) Experimental and Quasi-experimental Designs for Generalized Causal Inference (Houghton Mifflin, Boston).Google Scholar
• Simpson EH (1951) The interpretation of interaction in contingency tables. J. Royal Statist. Soc. Ser. B: Methodology 13(2):238–241.Google Scholar
• Steiner PM, Kim Y, Hall CE, Su D (2017) Graphical models for quasi-experimental designs. Sociol. Methods Res. 46(2):155–188.Crossref, Google Scholar
• Teele DL, ed. (2014) Field Experiments and Their Critics: Essays on the Uses and Abuses of Experimentation in the Social Sciences (Yale University Press, New York).Google Scholar
• Textor J, Hardt J, Knüppel S (2011) DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 22(5):745.Crossref, Google Scholar
• Tian J, Pearl J (2000) Probabilities of causation: Bounds and identification. Ann. Math. Artificial Intelligence 28(1–4):287–313.Crossref, Google Scholar
• Tingley D, Yamamoto T, Hirose K, Keele L, Imai K (2014) Mediation: R package for causal mediation analysis. J. Statist. Software 59(5).Google Scholar
• Westreich D, Greenland S (2013) The Table 2 Fallacy: Presenting and interpreting confounder and modifier coefficients. Amer. J. Epidemiology 177(4):292–298.Crossref, Google Scholar
• White H, Lu X (2011) Causal diagrams for treatment effect estimation with application to efficient covariate selection. Rev. Econom. Statist. 93(4):1453–1459.Crossref, Google Scholar
• Wooldridge JM (2002) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).Google Scholar
• Zhang H, Zhou S, Zhang K, Guan J (2017) Causal discovery using regression-based conditional independence tests. Proc. 31st AAAI Conf. Artificial Intelligence (AAAI, Palo Alto, CA), 3108–3115.Google Scholar
• Zheng Z, Pavlou PA (2010) Research note—Toward a causal interpretation from observational data: A new Bayesian networks method for structural models with latent variables. Inform. Systems Res. 21(2):365–391.Link, Google Scholar