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On the Lower Confidence Band for the Optimal Welfare in Policy Learning

Kirill Ponomarev, Vira Semenova

arXiv 9 Oct 2024 · Econometrics

arXiv:2410.07443 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We study inference on the optimal welfare in a policy learning problem and propose reporting a lower confidence band (LCB). A natural approach to constructing an LCB is to invert a one-sided t-test based on an efficient estimator for the optimal welfare. However, we show that for an empirically relevant class of DGPs, such an LCB can be first-order dominated by an LCB based on a welfare estimate for a suitable suboptimal treatment policy. We show that such first-order dominance is possible if and only if the optimal treatment policy is not “well-separated” from the rest, in the sense of the commonly imposed margin condition. When this condition fails, standard debiased inference methods are not applicable. We show that uniformly valid and easy-to-compute LCBs can be constructed analytically by inverting moment-inequality tests with the maximum and quasi-likelihood-ratio test statistics. As an empirical illustration, we revisit the National JTPA study and find that the proposed LCBs achieve reliable coverage and competitive length.

Citation extraction

67
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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
1Chernozhukov, V., S. Lee, and A. Rosen (2013) Intersection bounds: Estimation and inference1.00083100%
2Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice1.00074100%
3Andrews, D. W. and G. Soares (2010) Inference for parameters defined by moment inequalities using generalized moment selection0.9416483%
4Luedtke, A. and M. van der Laan (2016) Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy0.9285380%
5Tsybakov, A. B (2004) Optimal aggregation of classifiers in statistical learning0.8746467%
6Athey, S. and S. Wager (2021, January) (2021) Policy learning with observational data0.84333100%
7Mbakop, E. and M. Tabord-Meehan (2021, March) (2021) Model selection for treatment choice: Penalized welfare maximization0.84333100%
8Canay, I. A. and A. M. Shaikh (2017) Practical and theoretical advances in inference for partially identified models0.81142100%
9Hirano, K. and J. Porter (2012) Impossibility results for nondifferentiable functionals0.73732100%
10Romano, J. P., A. M. Shaikh, and M. Wolf (2014) A practical two-step method for testing moment inequalities0.73732100%

Showing the top 10 of 67 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
1Nonparametric Uniform Inference in Binary Classification and Policy Values0.64422
2Nonparametric Bayesian Policy Learning0.64422