The State of Applied Econometrics - Causality and Policy Evaluation

TL;DR

This paper reviews recent advances in econometrics for policy evaluation, emphasizing identification strategies, credibility enhancements, and machine learning methods for causal inference.

Contribution

It provides a comprehensive overview of new econometric techniques and best practices for applied researchers in policy evaluation.

Findings

Synthetic control and regression discontinuity methods improve causal inference.

Placebo, sensitivity, and robustness analyses enhance credibility.

Machine learning advances enable high-dimensional and heterogeneous effect estimation.

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.