Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge
arXiv 2 Aug 2022 · Econometrics · 18 citations (OpenAlex)
arXiv:2208.01300 · PDF · DOI · OpenAlex · Extracted main text
We revisit the problem of estimating the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) when control variables are available, either to render the instrumental variable (IV) suitably exogenous or to improve precision. Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so-called inverse probability weighted regression adjustment (IPWRA) estimators. By properly choosing models for the propensity score and outcome models, fitted values are ensured to be in the logical range determined by the response variable, producing DR estimators of LATE and LATT with appealing small sample properties. Inference is relatively straightforward both analytically and using the nonparametric bootstrap. Our DR LATE and DR LATT estimators work well in simulations. We also propose a DR version of the Hausman test that can be used to assess the unconfoundedness assumption through a comparison of different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance. Unlike the usual test that compares OLS and IV estimates, this procedure is robust to treatment effect heterogeneity.
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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 | Taubman, Allen, Wright, Baicker and Finkelstein (2014) Medicaid Increases Emergency-Department Use: Evidence from Oregon's Health Insurance Experiment | 1.000 | 10 | 4 | 100% |
| 2 | Soczyński and Wooldridge (2018) A General Double Robustness Result for Estimating Average Treatment Effects | 1.000 | 9 | 4 | 100% |
| 3 | Frölich (2007) Nonparametric IV Estimation of Local Average Treatment Effects with Covariates | 1.000 | 9 | 3 | 100% |
| 4 | Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 8 | 3 | 100% |
| 5 | Donald, Hsu and Lieli (2014) Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT | 1.000 | 7 | 4 | 100% |
| 6 | Abadie (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 0.965 | 10 | 5 | 90% |
| 7 | Finkelstein, Taubman, Wright, Bernstein, Gruber, Newhouse, Allen, Ba… (2012) The Oregon Health Insurance Experiment: Evidence from the First Year | 0.928 | 4 | 3 | 100% |
| 8 | Tan (2006) Regression and Weighting Methods for Causal Inference Using Instrumental Variables | 0.874 | 6 | 2 | 100% |
| 9 | Wooldridge (2007) Inverse Probability Weighted Estimation for General Missing Data Problems self | 0.874 | 6 | 2 | 100% |
| 10 | Belloni, Chernozhukov, Fernández-Val and Hansen (2017) Program Evaluation and Causal Inference with High-Dimensional Data | 0.843 | 3 | 3 | 100% |
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