arXiv 29 Sep 2020 · Econometrics · publishedJournal of Econometrics (2024) · 7 citations (OpenAlex)
arXiv:2009.13861 · PDF · DOI · OpenAlex · Extracted main text
For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to fully incorporate statistical independence (rather than mean independence) of instruments and a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services.
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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 | Mogstad, M., A. Santos, and A. Torgovitsky (2018) Using instrumental variables for inference about policy relevant treatment parameters | 1.000 | 37 | 9 | 100% |
| 2 | Heckman, J. J. and E. Vytlacil (2005) Structural equations, treatment effects, and econometric policy evaluation1 | 1.000 | 5 | 3 | 100% |
| 3 | Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.928 | 4 | 4 | 100% |
| 4 | Han, S. and S. Lee (2019) Estimation in a generalization of bivariate probit models with dummy endogenous regressors self | 0.928 | 4 | 3 | 100% |
| 5 | Balke, A. and J. Pearl (1997) Bounds on treatment effects from studies with imperfect compliance | 0.874 | 5 | 2 | 100% |
| 6 | Shaikh, A. M. and E. J. Vytlacil (2011) Partial identification in triangular systems of equations with binary dependent variables | 0.843 | 3 | 3 | 100% |
| 7 | Brinch, C. N., M. Mogstad, and M. Wiswall (2017) Beyond LATE with a discrete instrument | 0.843 | 3 | 3 | 100% |
| 8 | Kowalski, A. E (2021) Reconciling seemingly contradictory results from the Oregon health insurance experiment and the Massachusetts health reform | 0.843 | 3 | 3 | 100% |
| 9 | Manski, C. F. and J. V. Pepper (2000) Monotone instrumental variables: With an application to the returns to schooling | 0.811 | 4 | 2 | 100% |
| 10 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.737 | 3 | 2 | 100% |
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