Tadao Hoshino, Takahide Yanagi
arXiv 27 Jan 2020 · Econometrics · publishedJournal of Causal Inference (2022) · 3 citations (OpenAlex)
arXiv:2001.09560 · PDF · DOI · OpenAlex · Extracted main text
This paper studies treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. Using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. Based on our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE. We illustrate the usefulness of the proposed method with an application to economic returns to college education.
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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 | 5 | 3 | 100% |
| 2 | Heckman, J.J. and Vytlacil, E.J (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.874 | 5 | 2 | 100% |
| 3 | Heckman, J.J. and Vytlacil, E.J (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects | 0.811 | 4 | 2 | 100% |
| 4 | Mogstad, M., Torgovitsky, A., and Walters, C.R (2020) a | 0.811 | 4 | 2 | 100% |
| 5 | Kitamura, Y. and Laage, L (2018) Nonparametric analysis of finite mixtures | 0.644 | 2 | 2 | 100% |
| 6 | McLachlan, G. and Peel, D (2004) Finite Mixture Models | 0.644 | 2 | 2 | 100% |
| 7 | Mogstad, M. and Torgovitsky, A (2018) Identification and extrapolation of causal effects with instrumental variables | 0.644 | 2 | 2 | 100% |
| 8 | Hoshino, T. and Yanagi, T (2021) Treatment effect models with strategic interaction in treatment decisions self | 0.630 | 8 | 3 | 25% |
| 9 | Chen, X. and Christensen, T.M (2015) Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions | 0.511 | 2 | 2 | 50% |
| 10 | Lee, S. and Salanié, B (2018) Identifying effects of multivalued treatments | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Treatment Effect Models with Strategic Interaction in Treatment Decisions | 0.405 | 1 | 1 |
| 2 | Uniform Confidence Band for Marginal Treatment Effect Function | 0.405 | 1 | 1 |