This chapter relates the literature on the econometric evaluation of social programs to the literature in statistics on "causal inference". In it, we develop a general evaluation framework that addresses well-posed economic questions and analyzes agent choice rules and subjective evaluations of outcomes as well as the standard objective evaluations of outcomes. The framework recognizes uncertainty faced by agents and ex ante and ex post evaluations of programs. It also considers distributions of treatment effects. These features are absent from the statistical literature on causal inference. A prototypical model of agent choice and outcomes is used to illustrate the main ideas. We formally develop models for counterfactuals and causality that build on Cowles Commission econometrics. These models anticipate and extend the literature on causal inference in statistics. The distinction between fixing and conditioning that has recently entered the statistical literature was first developed by Cowles economists. Models of simultaneous causality were also developed by the Cowles group, as were notions of invariance to policy interventions. These basic notions are updated to nonlinear and nonparametric frameworks for policy evaluation more general than anything in the current statistical literature on "causal inference". A formal discussion of identification is presented and applied to clearly formulated choice models used to evaluate social programs.
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ReDIF This chapter was published in: J.J. Heckman & E.E. Leamer (ed.) Handbook of Econometrics, , chapter 70, pages , 2007.
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This chapter was published in the following book, which is listed on IDEAS: J.J. Heckman & E.E. Leamer (ed.), 2007.
"Handbook of Econometrics,"
Handbook of Econometrics,
Elsevier,
edition 1, volume 6, number 6b, September.
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