arXiv 9 Feb 2017 · Mathematics — Statistics Theory
arXiv:1702.02896 · PDF · Extracted main text
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.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | T. Kitagawa and A. Tetenov (2018) Who should be treated? Empirical welfare maximization methods for treatment choice | 1.000 | 21 | 5 | 100% |
| 2 | V. Chernozhukov, J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2016) Locally robust semiparametric estimation | 1.000 | 14 | 4 | 100% |
| 3 | K. Hirano and J. R. Porter (2009) Asymptotics for statistical treatment rules | 1.000 | 6 | 4 | 100% |
| 4 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 5 | J. Robins, A. Rotnitzky, and L. P. Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 0.843 | 3 | 3 | 100% |
| 6 | P. L. Bartlett and S. Mendelson (2002) Rademacher and Gaussian complexities: Risk bounds and structural results | 0.811 | 4 | 2 | 100% |
| 7 | D. A. Hirshberg and S. Wager (2018) Augmented minimax linear estimation | 0.811 | 4 | 2 | 100% |
| 8 | C. F. Manski (2009) Identification for Prediction and Decision | 0.811 | 4 | 2 | 100% |
| 9 | W. K. Newey (1994) The asymptotic variance of semiparametric estimators | 0.811 | 4 | 2 | 100% |
| 10 | A. Swaminathan and T. Joachims (2015) Batch learning from logged bandit feedback through counterfactual risk minimization | 0.811 | 4 | 2 | 100% |
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