Moment-Based Semiparametric Bayesian Causal Inference
Siddhartha Chib
Washington University in St. Louis
The UCI Department of Statistics is proud to present Siddhartha Chib, Harry C. Hartkopf Professor of Econometrics and Statistics, Washington University in St. Louis. More information is available at: https://www.stat.uci.edu/seminar-series/Title:
Moment-Based Semiparametric Bayesian Causal Inference
Abstract:
We consider the problem of prior-posterior analysis of causal parameters under minimal, core assumptions, in particular, unconditional and conditional moment restrictions on the unknown probability distribution of the outcomes. The framework is based on the theory of estimation and model comparison, under the nonparametric exponentially titled empirical likelihood, for moment restricted models, developed in Chib, Shin and Simoni (2018, JASA) and Chib, Shin and Simoni (2020, JRSS-B, in press). We provide illustrations of this approach to the estimation of the causal parameter in instrumental variables regressions, to the problem of average treatment effect (ATE) estimation under the conditional ignorability assumption, and the regression-discontinuity ATE estimation under a sharp-design.
We consider the problem of prior-posterior analysis of causal parameters under minimal, core assumptions, in particular, unconditional and conditional moment restrictions on the unknown probability distribution of the outcomes. The framework is based on the theory of estimation and model comparison, under the nonparametric exponentially titled empirical likelihood, for moment restricted models, developed in Chib, Shin and Simoni (2018, JASA) and Chib, Shin and Simoni (2020, JRSS-B, in press). We provide illustrations of this approach to the estimation of the causal parameter in instrumental variables regressions, to the problem of average treatment effect (ATE) estimation under the conditional ignorability assumption, and the regression-discontinuity ATE estimation under a sharp-design.
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