arXiv 14 Dec 2020 · Econometrics · publishedEconometric Theory (2024) · 2 citations (OpenAlex)
arXiv:2012.07624 · PDF · DOI · OpenAlex · Extracted main text
Consider a causal structure with endogeneity (i.e., unobserved confoundedness) in empirical data, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect (MTE, Bjorklund and Moffitt, 1987) as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization (EWM) rules as in Hirano and Porter (2020, Section 2.3). Focusing on the application to the EWM framework of Kitagawa and Tetenov (2018), we provide convergence rates of the worst case average welfare loss (regret) in the spirit of Manski (2004).
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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 E. Vytlacil (2001) Policy-Relevant Treatment Effects | 1.000 | 6 | 3 | 100% |
| 2 | Heckman, J. J. and E. Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 1.000 | 6 | 3 | 100% |
| 3 | Hirano, K. and J. R. Porter (2020) Asymptotic analysis of statistical decision rules in econometrics, in | 1.000 | 5 | 3 | 100% |
| 4 | Björklund, A. and R. Moffitt (1987) The Estimation of Wage Gains and Welfare Gains in Self-Selection Models | 0.928 | 4 | 3 | 100% |
| 5 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.909 | 16 | 5 | 75% |
| 6 | Heckman, J. J. and E. Vytlacil (2007) Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Esti… | 0.874 | 5 | 2 | 100% |
| 7 | Manski, C. F (2004) Statistical Treatment Rules for Heterogeneous Populations | 0.874 | 5 | 2 | 100% |
| 8 | Brinch, C. N., M. Mogstad, and M. Wiswall (2017) Beyond LATE with a Discrete Instrument | 0.737 | 3 | 2 | 100% |
| 9 | Carneiro, P., J. J. Heckman, and E. Vytlacil (2010) Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin | 0.737 | 3 | 2 | 100% |
| 10 | Carneiro, P. and S. Lee (2009) Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enro… | 0.737 | 3 | 2 | 100% |
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