arXiv 1 Oct 2019 · Econometrics · 2 citations (OpenAlex)
arXiv:1910.00641 · PDF · DOI · OpenAlex · Extracted main text
This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed covariates), instrumental variables (inducing a quasi-random shift in the treatment), difference-in-differences and changes-in-changes (exploiting changes in outcomes over time), as well as regression discontinuities and kinks (using changes in the treatment assignment at some threshold of a running variable). The chapter discusses methods particularly suited for data with many observations for a flexible (i.e. semi- or nonparametric) modeling of treatment effects, and/or many (i.e. high dimensional) observed covariates by applying machine learning to select and control for covariates in a data-driven way. This is not only useful for tackling confounding by controlling for instance for factors jointly affecting the treatment and the outcome, but also for learning effect heterogeneities across subgroups defined upon observable covariates and optimally targeting those groups for which the treatment is most effective.
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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 | Athey \ Imbens (2016) `Recursive partitioning for heterogeneous causal effects', Proceedings of the National Academy of Sciences 113, 7353–7360 | 0.811 | 4 | 2 | 100% |
| 2 | Athey, Tibshirani \ Wager (2019) `Generalized random forests', The Annals of Statistics 47, 1148–1178 | 0.737 | 3 | 2 | 100% |
| 3 | Athey \ Wager (2018) `Efficient policy learning', working paper, Stanford University | 0.644 | 2 | 2 | 100% |
| 4 | Cattaneo (2010) `Efficient semiparametric estimation of multi-valued treatment effects under ignorability', Journal of Econometrics 155, 138 – 154 | 0.644 | 2 | 2 | 100% |
| 5 | Dehejia \ Wahba (1999) `Causal effects in non-experimental studies: Reevaluating the evaluation of training programmes', Journal of American Statistica… | 0.644 | 2 | 2 | 100% |
| 6 | Heckman, Ichimura, Smith \ Todd (1998) `Characterizing selection bias using experimental data', Econometrica 66, 1017–1098 | 0.644 | 2 | 2 | 100% |
| 7 | Rosenbaum \ Rubin (1985) `Constructing a control group using multivariate matched sampling methods that incorporate the propensity score.', The American… | 0.644 | 2 | 2 | 100% |
| 8 | Zubizarreta (2015) `Stable weights that balance covariates for estimation with incomplete outcome data', Journal of the American Statistical Associ… | 0.644 | 2 | 2 | 100% |
| 9 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey \ Robins (2018) `Double/debiased machine learning for treatment and structural parameters', The Econometrics Journal 21, C1–C68 | 0.585 | 3 | 1 | 100% |
| 10 | Abadie (2003) `Semiparametric instrumental variable estimation of treatment response models', Journal of Econometrics 113, 231–263 | 0.511 | 2 | 1 | 100% |
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