arXiv 19 Jan 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 1 citations (OpenAlex)
arXiv:2001.06746 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. The DML estimator is semiparametric efficient and suitable for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
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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 | Heckman, J. J. and R. Pinto (2018) Unordered monotonicity | 0.941 | 6 | 3 | 83% |
| 2 | Kline, P. and C. R. Walters (2016) Evaluating public programs with close substitutes: The case of head start | 0.928 | 4 | 3 | 100% |
| 3 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.920 | 9 | 3 | 78% |
| 4 | Hahn, J (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.874 | 5 | 2 | 100% |
| 5 | Hong, H. and D. Nekipelov (2010) Semiparametric efficiency in nonlinear late models | 0.843 | 4 | 3 | 75% |
| 6 | Chen, X., H. Hong, and A. Tarozzi (2008) Semiparametric efficiency in gmm models with auxiliary data | 0.737 | 3 | 2 | 100% |
| 7 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 8 | Kitagawa, T (2015) A test for instrument validity | 0.737 | 3 | 2 | 100% |
| 9 | Finkelstein, A., S. Taubman, B. Wright, M. Bernstein, J. Gruber, J.… (2012) The oregon health insurance experiment: evidence from the first year | 0.693 | 6 | 1 | 100% |
| 10 | Newey, W. K (1994) The asymptotic variance of semiparametric estimators | 0.659 | 7 | 3 | 29% |
Showing the top 10 of 47 scored citations.