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Policy Transforms and Learning Optimal Policies

Thomas M. Russell

arXiv 20 Dec 2020 · Econometrics · 1 citations (OpenAlex)

arXiv:2012.11046 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We study the problem of choosing optimal policy rules in uncertain environments using models that may be incomplete and/or partially identified. We consider a policymaker who wishes to choose a policy to maximize a particular counterfactual quantity called a policy transform. We characterize learnability of a set of policy options by the existence of a decision rule that closely approximates the maximin optimal value of the policy transform with high probability. Sufficient conditions are provided for the existence of such a rule. However, learnability of an optimal policy is an ex-ante notion (i.e. before observing a sample), and so ex-post (i.e. after observing a sample) theoretical guarantees for certain policy rules are also provided. Our entire approach is applicable when the distribution of unobservables is not parametrically specified, although we discuss how semiparametric restrictions can be used. Finally, we show possible applications of the procedure to a simultaneous discrete choice example and a program evaluation example.

Citation extraction

116
references
255
in-text mentions
126
distinct cited
1
self-citations
64,067
main-text words

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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
1Koltchinskii, V (2011) Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: Ecole d’Eté de Probabilités de Saint-Flour XXXV…1.000124100%
2Van Der Vaart, A. W. and Wellner, J. A (1996) Weak convergence1.000114100%
3Chesher, A. and Rosen, A. M (2017) Generalized instrumental variable models1.000103100%
4Molchanov, I (2017) Theory of random sets1.00084100%
5Valiant, L. G (1984) A theory of the learnable1.00063100%
6Koltchinskii, V (2006) Local rademacher complexities and oracle inequalities in risk minimization0.87472100%
7Li, L (2019) Identification of structural and counterfactual parameters in a large class of structural econometric models0.87472100%
8Ekeland, I., Galichon, A., and Henry, M (2010) Optimal transportation and the falsifiability of incompletely specified economic models0.87462100%
9Schennach, S. M (2014) Entropic latent variable integration via simulation0.87452100%
10Galichon, A. and Henry, M (2011) Set identification in models with multiple equilibria0.81142100%

Showing the top 10 of 126 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
1Orthogonal Policy Learning Under Ambiguity0.51121
2Evidence Aggregation for Treatment Choice0.40511
3Optimal Decision Rules Under Partial Identification0.40511
4Optimal Decision Rules when Payoffs are Partially Identified0.40511
5Policy Learning under Endogeneity Using Instrumental Variables0.40511
6Policy Learning with Confidence$^$0.40511