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Welfare Analysis via Marginal Treatment Effects

Yuya Sasaki, Takuya Ura

arXiv 14 Dec 2020 · Econometrics · publishedEconometric Theory (2024) · 2 citations (OpenAlex)

arXiv:2012.07624 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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).

Citation extraction

37
references
96
in-text mentions
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Heckman, J. J. and E. Vytlacil (2001) Policy-Relevant Treatment Effects1.00063100%
2Heckman, J. J. and E. Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation1.00063100%
3Hirano, K. and J. R. Porter (2020) Asymptotic analysis of statistical decision rules in econometrics, in1.00053100%
4Björklund, A. and R. Moffitt (1987) The Estimation of Wage Gains and Welfare Gains in Self-Selection Models0.92843100%
5Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice0.90916575%
6Heckman, J. J. and E. Vytlacil (2007) Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Esti…0.87452100%
7Manski, C. F (2004) Statistical Treatment Rules for Heterogeneous Populations0.87452100%
8Brinch, C. N., M. Mogstad, and M. Wiswall (2017) Beyond LATE with a Discrete Instrument0.73732100%
9Carneiro, P., J. J. Heckman, and E. Vytlacil (2010) Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin0.73732100%
10Carneiro, P. and S. Lee (2009) Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enro…0.73732100%

Showing the top 10 of 37 scored citations.

Cited by, within the corpus

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Citing paperIntensityMentionsSections
1Policy Learning under Endogeneity Using Instrumental Variables0.87462
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3Personalized Subsidy Rules0.81142
4Policy Learning under Unobserved Confounding: A Robust and Efficient Approach0.51121
5Policy design in experiments with unknown interference0.40511
6Policy Learning with New Treatments0.40511
7Stochastic treatment choice with empirical welfare updating0.40511
8Multi-cell experiments for marginal treatment effect estimation of digital ads0.40511
9Uniform Confidence Band for Marginal Treatment Effect Function0.40511