Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge
arXiv 15 Apr 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 4 citations (OpenAlex)
arXiv:2204.07672 · PDF · DOI · OpenAlex · Extracted main text
Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie's (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
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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 | Frölich (2007) Nonparametric IV Estimation of Local Average Treatment Effects with Covariates | 1.000 | 11 | 3 | 100% |
| 2 | Angrist and Evans (1998) Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size | 1.000 | 10 | 3 | 100% |
| 3 | Tan (2006) Regression and Weighting Methods for Causal Inference Using Instrumental Variables | 1.000 | 9 | 3 | 100% |
| 4 | Uysal (2011) Three Essays on Doubly Robust Estimation Methods self | 1.000 | 7 | 3 | 100% |
| 5 | Heiler (2022) Efficient Covariate Balancing for the Local Average Treatment Effect | 0.981 | 18 | 4 | 94% |
| 6 | Sant'Anna, Song and Xu (2022) Covariate Distribution Balance via Propensity Scores | 0.928 | 4 | 3 | 100% |
| 7 | Abadie (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 0.894 | 28 | 4 | 71% |
| 8 | Card (1995) Using Geographic Variation in College Proximity to Estimate the Return to Schooling | 0.874 | 18 | 2 | 100% |
| 9 | Angrist (1990) Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records | 0.874 | 8 | 2 | 100% |
| 10 | Imai and Ratkovic (2014) Covariate Balancing Propensity Score | 0.874 | 6 | 2 | 100% |
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