arXiv 4 Dec 2020 · Econometrics · publishedJournal of Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2012.02390 · PDF · DOI · OpenAlex · Extracted main text
A fundamental question underlying the literature on partial identification is: what can we learn about parameters that are relevant for policy but not necessarily point-identified by the exogenous variation we observe? This paper provides an answer in terms of sharp, analytic characterizations and bounds for an important class of policy-relevant treatment effects, consisting of marginal treatment effects and linear functionals thereof, in the latent index selection model as formalized in Vytlacil (2002). The sharp bounds use the full content of identified marginal distributions, and analytic derivations rely on the theory of stochastic orders. The proposed methods also make it possible to sharply incorporate new auxiliary assumptions on distributions into the latent index selection framework. Empirically, I apply the methods to study the effects of Medicaid on emergency room utilization in the Oregon Health Insurance Experiment, showing that the predictions from extrapolations based on a distribution assumption (rank similarity) differ substantively and consistently from existing extrapolations based on a parametric mean assumption (linearity). This underscores the value of utilizing the model's full empirical content in combination with auxiliary assumptions.
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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 Vytlacil, E (2005) Structural Equations, Treatment Efects, and Econometric Policy Evaluation | 1.000 | 11 | 5 | 100% |
| 2 | Kowalski, A (2016) Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Exp… | 1.000 | 10 | 4 | 100% |
| 3 | Vytlacil, E (2002) Independence, Monotonicity, and Latent Index Models: An Equivalence Result | 1.000 | 9 | 5 | 100% |
| 4 | Imbens, G.W. and Angrist, J (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 7 | 4 | 100% |
| 5 | Brinch, C.N., Mogstad, M., and Wiswall, M (2017) Beyond LATE with a Discrete Instrument | 1.000 | 5 | 4 | 100% |
| 6 | Heckman, J.J. and Vytlacil, E.J (1999) Local Instrumental Variables and Latent Variable Models for Identifying and Bounding Treatment Effects | 1.000 | 5 | 4 | 100% |
| 7 | Chernozhukov, V., and Hansen, C (2005) An IV Model of Quantile Treatment Effects | 1.000 | 5 | 3 | 100% |
| 8 | Lee, D.S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 1.000 | 5 | 3 | 100% |
| 9 | Mogstad, M., Santos, A., and Torgovitsky, A (2018) Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters | 1.000 | 5 | 3 | 100% |
| 10 | Fan, Y., and Park, S (2010) Sharp Bounds on the Distribution of the Treatment Effect and Their Statistical Inference | 0.830 | 7 | 3 | 57% |
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