The State of Applied Econometrics - Causality and Policy Evaluation

Susan Athey and Guido Imbens

Papers from arXiv.org

Abstract: In this paper we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, where in each case we highlight recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses to make the identification strategies more credible. These include placebo analyses as well as sensitivity and robustness analyses. Third, we discuss recent advances in machine learning methods for causal effects. These advances include methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogeneous treatment effects.

Date: 2016-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Citations: View citations in EconPapers (29)

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Journal Article: The State of Applied Econometrics: Causality and Policy Evaluation (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1607.00699

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