arXiv 24 Jun 2019 · Econometrics · publishedThe Review of Economic Studies (2024) · 19 citations (OpenAlex)
arXiv:1906.10258 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analog of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknown propensity scores and cast the optimization problem into a mixed-integer linear program, which can be solved using off-the-shelf algorithms. I derive a strong set of guarantees on regret, i.e., the difference between the maximum attainable welfare and the welfare evaluated at the estimated policy. The proposed method presents attractive features for applications: (i) it does not require network information of the target population; (ii) it exploits heterogeneity in treatment effects for targeting individuals; (iii) it does not rely on the correct specification of a particular structural model; and (iv) it accommodates constraints on the policy function. An application for targeting information on social networks illustrates the advantages of the method.
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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 | Cai, J., A. De Janvry, and E. Sadoulet (2015) Social networks and the decision to insure | 1.000 | 31 | 3 | 100% |
| 2 | Aronow, P. M. and C. Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 1.000 | 5 | 3 | 100% |
| 3 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.923 | 14 | 5 | 79% |
| 4 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? Empirical welfare maximization methods for treatment choice | 0.815 | 24 | 7 | 54% |
| 5 | Wainwright, M. J (2019) High-dimensional statistics: A non-asymptotic viewpoint, Volume 48 | 0.811 | 4 | 2 | 100% |
| 6 | Brooks, R. L (1941) On colouring the nodes of a network | 0.737 | 4 | 3 | 50% |
| 7 | De Paula, Á., S. Richards-Shubik, and E. Tamer (2018) Identifying preferences in networks with bounded degree | 0.737 | 3 | 2 | 100% |
| 8 | Kempe, D., J. Kleinberg, and É. Tardos (2003) Maximizing the spread of influence through a social network | 0.737 | 3 | 2 | 100% |
| 9 | Kitagawa, T. and G. Wang (2020) Who should get vaccinated? individualized allocation of vaccines over sir network | 0.737 | 3 | 2 | 100% |
| 10 | Leung, M. P (2020) Treatment and spillover effects under network interference | 0.737 | 3 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.