Conditional Average Treatment Effects and Decision Making

49 Pages Posted: 1 Feb 2014 Last revised: 22 Apr 2014

Date Written: March 26, 2014

Abstract

This paper develops a decision making empirical method to evaluate welfare programs accounting for heterogeneity of impacts. We find outcome predictive distributions for different subgroups of the population and use a characterization of second order stochastic dominance to give a policy recommendation conditional on covariates with minimal requirements on the social planner's utility function. Further, we can estimate quantile treatment effects within subgroups of the population. We apply this method to the Connecticut's Jobs First program and find subgroups for which the program did not maximize welfare even though some statistics may suggest the opposite and vice-versa.

Keywords: conditional average treatment effects, heterogeneous impacts, hierarchical Bayesian model, decision making, stochastic dominance

JEL Classification: C11, D31, I38, J31

Suggested Citation

Samano, Mario, Conditional Average Treatment Effects and Decision Making (March 26, 2014). Available at SSRN: https://ssrn.com/abstract=2388364 or http://dx.doi.org/10.2139/ssrn.2388364

Mario Samano (Contact Author)

HEC Montreal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada

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