arXiv 4 Sep 2020 · Econometrics · publishedBiometrika (2021) · 5 citations (OpenAlex)
arXiv:2009.02314 · PDF · DOI · OpenAlex · Extracted main text
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
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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 (2000) The role of the propensity score in estimating dose-response functions | 1.000 | 5 | 4 | 100% |
| 2 | Rosenbaum, P. R. and Rubin, D. B (1983) The central role of the propensity score in observational studies for causal effects | 1.000 | 5 | 3 | 100% |
| 3 | Ao, W., Calonico, S., and Lee, Y. Y (2021) Multivalued treatments and decomposition analysis: An application to the WIA program | 0.644 | 2 | 2 | 100% |
| 4 | Frölich, M (2004) Programme evaluation with multiple treatments | 0.644 | 2 | 2 | 100% |
| 5 | Graham, B. S. and Pinto, C. C. D. X (2018) Semiparametrically efficient estimation of the average linear regression function | 0.644 | 2 | 2 | 100% |
| 6 | Athey, S. and Imbens, G. W (2017) The state of applied econometrics: causality and policy evaluation | 0.405 | 1 | 1 | 100% |
| 7 | Becker, S. O. and Egger, P. H (2013) Endogenous product versus process innovation and a firm s propensity to export | 0.405 | 1 | 1 | 100% |
| 8 | Blundell, R., and Powell, J. L (2003) Endogeneity in nonparametric and semiparametric regression models | 0.405 | 1 | 1 | 100% |
| 9 | Breusch, T. S (1986) Hypothesis testing in unidentified models | 0.405 | 1 | 1 | 100% |
| 10 | Cattaneo, M (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification of Treatment Effects under Limited Exogenous Variation | 0.874 | 6 | 4 |