Orthogonal Policy Learning Under Ambiguity

Riccardo D'Adamo

Papers from arXiv.org

Abstract: This paper studies the problem of estimating individualized treatment rules when treatment effects are partially identified, as it is often the case with observational data. By drawing connections between the treatment assignment problem and classical decision theory, we characterize several notions of optimal treatment policies in the presence of partial identification. Our unified framework allows to incorporate user-defined constraints on the set of allowable policies, such as restrictions for transparency or interpretability, while also ensuring computational feasibility. We show how partial identification leads to a new policy learning problem where the objective function is directionally -- but not fully -- differentiable with respect to the nuisance first-stage. We then propose an estimation procedure that ensures Neyman-orthogonality with respect to the nuisance components and we provide statistical guarantees that depend on the amount of concentration around the points of non-differentiability in the data-generating-process. The proposed methods are illustrated using data from the Job Partnership Training Act study.

Date: 2021-11, Revised 2022-12
New Economics Papers: this item is included in nep-big and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2111.10904 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2111.10904

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().