Alexandre Belloni, Fei Fang, Alexander Volfovsky
arXiv 7 Dec 2022 · Statistics — Machine Learning · 3 citations (OpenAlex)
arXiv:2212.03683 · PDF · DOI · OpenAlex · Extracted main text
Estimating causal effects has become an integral part of most applied fields. In this work we consider the violation of the classical no-interference assumption with units connected by a network. For tractability, we consider a known network that describes how interference may spread. Unlike previous work the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different (local) sub-networks and the assigned treatments. We study estimators for the average direct treatment effect on the treated in such a setting under additive treatment effects. We establish rates of convergence and distributional results. The proposed estimators considers all possible radii for each (local) treatment assignment pattern. In contrast to previous work, we approximate the relevant network interference patterns that lead to good estimates of the interference. To handle feature engineering, a key innovation is to propose the use of synthetic treatments to decouple the dependence. We provide simulations, an empirical illustration and insights for the general study of interference.
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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 | Sussman \ Airoldi (2017) `Elements of estimation theory for causal effects in the presence of network interference', arXiv:1702.03578 | 1.000 | 8 | 3 | 100% |
| 2 | Aronow \ Samii (2017) `Estimating average causal effects under general interference, with application to a social network experiment', The Annals of A… | 1.000 | 6 | 4 | 100% |
| 3 | McDiarmid et al (1989) `On the method of bounded differences', Surveys in combinatorics 141(1), 148–188 | 0.843 | 3 | 3 | 100% |
| 4 | Awan, Morucci, Orlandi, Roy, Rudin \ Volfovsky (2020) Almost-matching-exactly for treatment effect estimation under network interference, in `International Conference on Artificial I… | 0.811 | 4 | 2 | 100% |
| 5 | Jagadeesan, Pillai \ Volfovsky (2020) `Designs for estimating the treatment effect in networks with interference', Annals of Statistics 48(2), 679–712 | 0.811 | 4 | 2 | 100% |
| 6 | Manski (2013) `Identification of treatment response with social interactions', The Econometrics Journal 16(1), S1–S23 | 0.737 | 3 | 2 | 100% |
| 7 | Belloni \ Oliveira (2018) `A high dimensional central limit theorem for martingales, with applications to context tree models', arXiv:1809.02741 | 0.644 | 2 | 2 | 100% |
| 8 | Forastiere, Airoldi \ Mealli (2021) `Identification and estimation of treatment and interference effects in observational studies on networks', Journal of the Ameri… | 0.644 | 2 | 2 | 100% |
| 9 | Karwa \ Airoldi (2018) `A systematic investigation of classical causal inference strategies under mis-specification due to network interference', arXiv… | 0.644 | 2 | 2 | 100% |
| 10 | Leung (2022) `Causal inference under approximate neighborhood interference', Econometrica 90(1), 267–293 | 0.644 | 2 | 2 | 100% |
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