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Optimal Decision Rules Under Partial Identification

Kohei Yata

arXiv 9 Nov 2021 · Econometrics · 3 citations (OpenAlex)

arXiv:2111.04926 · PDF · DOI · OpenAlex · Extracted main text

Abstract

I consider a class of statistical decision problems in which the policymaker must decide between two policies to maximize social welfare (e.g., the population mean of an outcome) based on a finite sample. The framework introduced in this paper allows for various types of restrictions on the structural parameter (e.g., the smoothness of a conditional mean potential outcome function) and accommodates settings with partial identification of social welfare. As the main theoretical result, I derive a finite-sample optimal decision rule under the minimax regret criterion. This rule has a simple form, yet achieves optimality among all decision rules; no ad hoc restrictions are imposed on the class of decision rules. I apply my results to the problem of whether to change an eligibility cutoff in a regression discontinuity setup, and illustrate them in an empirical application to a school construction program in Burkina Faso.

Citation extraction

61
references
254
in-text mentions
68
distinct cited
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self-citations
34,150
main-text words

appendix boundary found by appendix_command · 41% of the source is main text. Read the extracted text to check this.

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
1Ishihara, T. and Kitagawa, T (2024) Evidence Aggregation for Treatment Choice1.000145100%
2Hirano, K. and Porter, J. R (2009) Asymptotics for Statistical Treatment Rules1.00073100%
3–- (2012) Minimax Regret Treatment Choice with Covariates or with Limited Validity of Experiments0.98421495%
4Donoho, D. L (1994) Statistical Estimation and Optimal Recovery0.95244786%
5Low, M. G (1995) Bias-Variance Tradeoffs in Functional Estimation Problems0.94613485%
6Stoye, J (2009) Minimax Regret Treatment Choice with Finite Samples0.9416383%
7Tetenov, A (2012) Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria0.9285480%
8Imbens, G. and Wager, S (2019) Optimized Regression Discontinuity Designs0.9285380%
9Kazianga, H., Levy, D., Linden, L. L. and Sloan, M (2013) Girl-Friendly0.874122100%
10–- (2004) Statistical Treatment Rules for Heterogeneous Populations0.87452100%

Showing the top 10 of 68 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Robust Bayes Treatment Choice with Partial Identification1.00074
2Dynamically Consistent Statistical Decisions1.00063
3Evidence Aggregation for Treatment Choice1.00053
4Bandwidth Selection for Treatment Choice with Binary Outcomes1.00053
5Statistical Decisions and Partial Identification: With Application to Boundary Discontinuity Design0.87462
6Extrapolating Away from the Cutoff in Regression Discontinuity Designs0.84333
7Robust Estimation and Inference in Panels with Interactive Fixed Effects0.64422
8Shrinkage Methods for Treatment Choice0.64422
9Geometric Control of Decisions' Affordability0.58531
10Stochastic treatment choice with empirical welfare updating0.51121