Zhaonan Qu, Ruoxuan Xiong, Jizhou Liu, Guido Imbens
arXiv 26 Jul 2021 · Statistics — Methodology · publishedJournal of Business and Economic Statistics (2026) · 8 citations (OpenAlex)
arXiv:2107.12420 · PDF · DOI · OpenAlex · Extracted main text
In many observational studies in social science and medicine, subjects or units are connected, and one unit's treatment and attributes may affect another's treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous and needs to be modeled explicitly. In this paper, we focus on the partial interference setting, and only restrict units to be exchangeable conditional on observable characteristics. Under this framework, we propose generalized augmented inverse propensity weighted (AIPW) estimators for general causal estimands that include heterogeneous direct and spillover effects. We show that they are semiparametric efficient and robust to heterogeneous interference as well as model misspecifications. We apply our methods to the Add Health dataset to study the direct effects of alcohol consumption on academic performance and the spillover effects of parental incarceration on adolescent well-being.
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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 | Vazquez-Bare, G (2022) Identification and estimation of spillover effects in randomized experiments | 1.000 | 6 | 3 | 100% |
| 2 | Tchetgen, E. J. T. and VanderWeele, T. J (2012) On causal inference in the presence of interference | 1.000 | 5 | 3 | 100% |
| 3 | Forastiere, L., Airoldi, E. M., and Mealli, F (2020) Identification and estimation of treatment and interference effects in observational studies on networks | 0.969 | 11 | 5 | 91% |
| 4 | Hudgens, M. G. and Halloran, M. E (2008) Toward causal inference with interference | 0.941 | 6 | 3 | 83% |
| 5 | Robins, J. M., Rotnitzky, A., and Zhao, L. P (1994) Estimation of regression coefficients when some regressors are not always observed | 0.928 | 5 | 3 | 80% |
| 6 | Liu, L., Hudgens, M. G., and Becker-Dreps, S (2016) On inverse probability-weighted estimators in the presence of interference | 0.928 | 4 | 3 | 100% |
| 7 | Park, C. and Kang, H (2022) Efficient semiparametric estimation of network treatment effects under partial interference | 0.822 | 9 | 4 | 56% |
| 8 | Barkley, B. G., Hudgens, M. G., Clemens, J. D., Ali, M., Emch, M. E.… (2020) Causal inference from observational studies with clustered interference, with application to a cholera vaccine study | 0.737 | 3 | 2 | 100% |
| 9 | Sobel, M. E (2006) What do randomized studies of housing mobility demonstrate? causal inference in the face of interference | 0.737 | 3 | 2 | 100% |
| 10 | Hahn, J (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 73 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Graph Neural Networks for Generalized Mundlak Estimator under Network Confounding | 0.405 | 1 | 1 |