arXiv 4 Nov 2023 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2311.02467 · PDF · DOI · OpenAlex · Extracted main text
Although there is now a large literature on policy evaluation and learning, much of the prior work assumes that the treatment assignment of one unit does not affect the outcome of another unit. Unfortunately, ignoring interference can lead to biased policy evaluation and ineffective learned policies. For example, treating influential individuals who have many friends can generate positive spillover effects, thereby improving the overall performance of an individualized treatment rule (ITR). We consider the problem of evaluating and learning an optimal ITR under clustered network interference (also known as partial interference), where clusters of units are sampled from a population and units may influence one another within each cluster. Unlike previous methods that impose strong restrictions on spillover effects, such as anonymous interference, the proposed methodology only assumes a semiparametric structural model, where each unit's outcome is an additive function of individual treatments within the cluster. Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR. We show that this estimator is substantially more efficient than the standard inverse probability weighting estimator, which does not impose any assumption about spillover effects. We derive the finite-sample regret bound for a learned ITR, showing that the use of our efficient evaluation estimator leads to the improved performance of learned policies. We consider both experimental and observational studies, and for the latter, we develop a doubly robust estimator that is semiparametrically efficient and yields an optimal regret bound. Finally, we conduct simulation and empirical studies to illustrate the advantages of the proposed methodology.
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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 | Athey, S. and S. Wager (2021) Policy learning with observational data | 1.000 | 9 | 5 | 100% |
| 2 | Viviano, D (2024) Policy targeting under network interference | 1.000 | 6 | 3 | 100% |
| 3 | Liu, L., M. G. Hudgens, and S. Becker-Dreps (2016) On inverse probability-weighted estimators in the presence of interference | 1.000 | 5 | 3 | 100% |
| 4 | Hudgens, M. and M. Halloran (2008) Toward causal inference with interference | 1.000 | 5 | 3 | 100% |
| 5 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.965 | 10 | 5 | 90% |
| 6 | Park, C. and H. Kang (2022) Efficient semiparametric estimation of network treatment effects under partial interference | 0.928 | 4 | 3 | 100% |
| 7 | Zhou, Z., S. Athey, and S. Wager (2023) Offline multi-action policy learning: Generalization and optimization | 0.855 | 8 | 5 | 62% |
| 8 | Imai, K., Z. Jiang, and A. Malani (2021) Causal inference with interference and noncompliance in two-stage randomized experiments self | 0.843 | 3 | 3 | 100% |
| 9 | Park, C., G. Chen, M. Yu, and H. Kang (2023) Minimum resource threshold policy under partial interference | 0.811 | 4 | 2 | 100% |
| 10 | Chernozhukov, V., M. Demirer, G. Lewis, and V. Syrgkanis (2019) Semi-parametric efficient policy learning with continuous actions | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Ranking Treatment Saturations under Clustered Network Interference | 0.405 | 1 | 1 |