Manu Navjeevan, Rodrigo Pinto, Andres Santos
arXiv 8 Oct 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2310.05311 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a class of potential outcomes models characterized by three main features: (i) Unobserved heterogeneity can be represented by a vector of potential outcomes and a type describing the manner in which an instrument determines the choice of treatment; (ii) The availability of an instrumental variable that is conditionally independent of unobserved heterogeneity; and (iii) The imposition of convex restrictions on the distribution of unobserved heterogeneity. The proposed class of models encompasses multiple classical and novel research designs, yet possesses a common structure that permits a unifying analysis of identification and estimation. In particular, we establish that these models share a common necessary and sufficient condition for identifying certain causal parameters. Our identification results are constructive in that they yield estimating moment conditions for the parameters of interest. Focusing on a leading special case of our framework, we further show how these estimating moment conditions may be modified to be doubly robust. The corresponding double robust estimators are shown to be asymptotically normally distributed, bootstrap based inference is shown to be asymptotically valid, and the semi-parametric efficiency bound is derived for those parameters that are root-n estimable. We illustrate the usefulness of our results for developing, identifying, and estimating causal models through an empirical evaluation of the role of mental health as a mediating variable in the Moving To Opportunity 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 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 1.000 | 7 | 4 | 100% |
| 2 | Mogstad, M., Torgovitsky, A. and Walters, C. R (2021) The causal interpretation of two-stage least squares with multiple instrumental variables | 1.000 | 7 | 4 | 100% |
| 3 | Kline, P. and Walters, C. R (2016) Evaluating public programs with close substitutes: The case of head start | 1.000 | 6 | 4 | 100% |
| 4 | Le Cam, L. and Yang, G. L (1988) On the preservation of local asymptotic normality under information loss | 1.000 | 5 | 3 | 100% |
| 5 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 0.928 | 4 | 3 | 100% |
| 6 | Chen, X. and Santos, A (2018) Overidentification in regular models self | 0.874 | 8 | 2 | 100% |
| 7 | Pinto, R (2021) Beyond intention to treat: Using the incentives in moving to opportunity to identify neighborhood effects self | 0.843 | 3 | 3 | 100% |
| 8 | Bickel, P. J., Klassen, C. A., Ritov, Y. and Wellner, J. A (1993) Efficient and Adaptive Estimation for Semiparametric Models | 0.811 | 4 | 2 | 100% |
| 9 | Heckman, J. J. and Vytlacil, E (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 0.811 | 4 | 2 | 100% |
| 10 | Orr, L., Feins, J. D., Jacob, R. and Beecroft, E (2003) Moving to Opportunity Interim Impacts Evaluation | 0.737 | 3 | 2 | 100% |
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