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Topics in Causal Inference DRP Final Presentation Omkar A. Katta - PowerPoint PPT Presentation

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Topics in Causal Inference DRP Final Presentation Omkar A. Katta April 30, 2020 Outline I. Introduction to Rubin Causal Model II. Methods and Challenges III. Frontier IV. References Introduction to Rubin Causal Model 1/12 CORRELATION IS

  1. Topics in Causal Inference DRP Final Presentation Omkar A. Katta April 30, 2020

  2. Outline I. Introduction to Rubin Causal Model II. Methods and Challenges III. Frontier IV. References Introduction to Rubin Causal Model 1/12

  3. CORRELATION IS NOT CAUSATION. Introduction to Rubin Causal Model 1/12

  4. Causation • Outcome Y • Possible Cause X • ∆ X → ∆ Y ? • ceteris paribus - all else equal • The changes in Y can only be attributed to the differences in X . Introduction to Rubin Causal Model 2/12

  5. Rubin Causal Model • Treatment Indicator: D i = ✶ ( i is treated) • Y i (1) is outcome if i is treated. • Y i (0) is outcome if i is untreated. • We want: E ( Y i (1) − Y i (0)) ceteris paribus • Fundamental Problem of Causal Inference Y i = D i Y i (1) + (1 − D i ) Y i (0) = Y i ( D i ) . Introduction to Rubin Causal Model 3/12

  6. Outline I. Introduction to Rubin Causal Model II. Methods and Challenges III. Frontier IV. References Methods and Challenges 4/12

  7. What is the causal impact of a treatment on the outcome? • Randomization • Differences-in-Differences • Find a counterfactual . • First difference: washes out systematic differences. • Second difference: average causal effect. • Regression Discontinuity Design • An exogeneous shock • E.g., merit-based scholarship, SAT cutoffs • Instrumental Variables, Structural Models, Propensity Score Matching, . . . Methods and Challenges 5/12

  8. What could go wrong? • We cannot randomize. • Selection Bias • Omitted Variable Bias • Simultaneity • Violation of Stable Unit Treatment Value Assumption ( SUTVA ) • Quantification of Uncertainty Methods and Challenges 6/12

  9. Outline I. Introduction to Rubin Causal Model II. Methods and Challenges III. Frontier IV. References Frontier 7/12

  10. Frontier • Is randomization all that great? • Dynamic Potential Outcomes Model • Non-parametric and Semi-parametric designs • Machine Learning, Network Theory 1. unsupervised learning: heterogeneous treatment effects 2. prediction techniques: synthetic control 3. Big Data: finite population uncertainty 4. networks model interference effects: relax SUTVA and adopt NIA 5. model-driven vs. data-driven ; standard errors and statistical properties Frontier 8/12

  11. Outline I. Introduction to Rubin Causal Model II. Methods and Challenges III. Frontier IV. References References 9/12

  12. References Abadie, Alberto and Javier Gardeazabal (2003). “The economic costs of conflict: A case study of the Basque Country”. In: American economic review 93.1, pp. 113–132. Abadie, Alberto et al. (2014). Finite population causal standard errors . Tech. rep. National Bureau of Economic Research. Athey, S and GW Imbens (2019). “Machine Learning Methods Economists Should Know About”. In: arXiv preprint arXiv: 1903.10075 . Athey, Susan (2015). “Machine learning and causal inference for policy evaluation”. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , pp. 5–6. Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan (2004). “How much should we trust differences-in-differences estimates?” In: The Quarterly journal of economics 119.1, pp. 249–275. References 10/12

  13. References II Chalfin, Aaron et al. (2016). “Productivity and selection of human capital with machine learning”. In: American Economic Review 106.5, pp. 124–27. Chandler, Dana, Steven D Levitt, and John A List (2011). “Predicting and preventing shootings among at-risk youth”. In: American Economic Review 101.3, pp. 288–92. Einav, Liran and Jonathan Levin (2014). “Economics in the age of big data”. In: Science 346.6210, p. 1243089. Jagadeesan, Ravi, Natesh Pillai, and Alexander Volfovsky (2017). “Designs for estimating the treatment effect in networks with interference”. In: arXiv preprint arXiv:1705.08524 . Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: an applied econometric approach”. In: Journal of Economic Perspectives 31.2, pp. 87–106. References 11/12

  14. References III Sussman, Daniel L and Edoardo M Airoldi (2017). “Elements of estimation theory for causal effects in the presence of network interference”. In: arXiv preprint arXiv:1702.03578 . Torgovitsky, Alexander (2019). “Nonparametric inference on state dependence in unemployment”. In: Econometrica 87.5, pp. 1475–1505. References 12/12

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