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Policy Learning With Observational Data
Author(s) -
Athey Susan,
Wager Stefan
Publication year - 2021
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta15732
Subject(s) - regret , observational study , estimator , leverage (statistics) , observable , instrumental variable , computer science , set (abstract data type) , causal inference , variety (cybernetics) , econometrics , budget constraint , mathematical optimization , observational equivalence , selection (genetic algorithm) , simplicity , mathematics , economics , machine learning , artificial intelligence , statistics , microeconomics , philosophy , physics , epistemology , quantum mechanics , programming language
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.