arXiv 3 Jul 2016 · Statistics — Methodology · publishedThe Journal of Economic Perspectives (2017) · 201 citations (OpenAlex)
arXiv:1607.00699 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Guido W Imbens and Donald B Rubin (2015) Causal Inference in Statistics, Social, and Biomedical Sciences self | 0.836 | 8 | 2 | 88% |
| 2 | S. Calonico, Matias Cattaneo, and Rocio Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.737 | 4 | 2 | 75% |
| 3 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california�s tobacco control program | 0.693 | 7 | 1 | 100% |
| 4 | Paul R Rosenbaum and Donald B Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.693 | 5 | 1 | 100% |
| 5 | Joshua Angrist and Ivan Fernandez-Val (2010) Extrapolate-ing: External validity and overidentification in the late framework | 0.644 | 4 | 1 | 100% |
| 6 | Susan Athey, Dean Eckles, and Guido Imbens (2015) Exact p-values for network interference, 2015 self | 0.644 | 4 | 1 | 100% |
| 7 | Marinho Bertanha and Guido Imbens (2015) External validity in fuzzy regression discontinuity designs self | 0.644 | 4 | 1 | 100% |
| 8 | Joshua D Angrist (2004) Treatment effect heterogeneity in theory and practice | 0.585 | 3 | 1 | 100% |
| 9 | Joshua D Angrist and Miikka Rokkanen (2015) Wanna get away? regression discontinuity estimation of exam school effects away from the cutoff | 0.585 | 3 | 1 | 100% |
| 10 | David Card, David Lee, Z Pei, and Andrea Weber (2015) Inference on causal effects in a generalized regression kink design | 0.585 | 3 | 1 | 100% |
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