Toru Kitagawa, Hugo Lopez, Jeff Rowley
arXiv 3 Nov 2022 · Econometrics
arXiv:2211.01537 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel method to estimate individualised treatment assignment rules. The method is designed to find rules that are stochastic, reflecting uncertainty in estimation of an assignment rule and about its welfare performance. Our approach is to form a prior distribution over assignment rules, not over data generating processes, and to update this prior based upon an empirical welfare criterion, not likelihood. The social planner then assigns treatment by drawing a policy from the resulting posterior. We show analytically a welfare-optimal way of updating the prior using empirical welfare; this posterior is not feasible to compute, so we propose a variational Bayes approximation for the optimal posterior. We characterise the welfare regret convergence of the assignment rule based upon this variational Bayes approximation, showing that it converges to zero at a rate of ln(n)/sqrt(n). We apply our methods to experimental data from the Job Training Partnership Act Study to illustrate the implementation of our methods.
appendix boundary found by appendix_command · 46% of the source is main text. Read the extracted text to check this.
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 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice self | 0.958 | 25 | 6 | 88% |
| 2 | Alquier, P., J. Ridgway, and N. Chopin (2016) On the properties of variational approximations of Gibbs posteriors | 0.909 | 8 | 3 | 75% |
| 3 | Bégin, L., P. Germain, F. Laviolette, and J.-F. Roy (2016) PAC-Bayesian bounds based on the Rényi divergence, in | 0.909 | 8 | 3 | 75% |
| 4 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.874 | 5 | 2 | 100% |
| 5 | Germain, P., A. Lacasse, F. Laviolette, and M. Marchand (2009) PAC-Bayesian learning of linear classifiers, in | 0.843 | 3 | 3 | 100% |
| 6 | McAllester, D. A (2003) PAC-Bayesian stochastic model selection | 0.811 | 4 | 2 | 100% |
| 7 | Chamberlain, G (2011) Bayesian aspects of treatment choice | 0.737 | 3 | 2 | 100% |
| 8 | Bloom, H. S., L. L. Orr, S. H. Bell, G. Cave, F. Doolittle, W. Lin,… (1997) The benefits and costs of JTPA Title II-A programs: Key findings from the National Job Training Partnership Act study | 0.737 | 3 | 2 | 100% |
| 9 | Maurer, A (2004) A note on the PAC Bayesian theorem | 0.693 | 6 | 3 | 33% |
| 10 | Bissiri, P. G., C. C. Holmes, and S. G. Walker (2016) A general framework for updating belief distributions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 78 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.