Yuchen Hu, Shuangning Li, Stefan Wager
arXiv 8 Apr 2021 · Statistics — Methodology · publishedBiometrika (2022) · 27 citations (OpenAlex)
arXiv:2104.03802 · PDF · DOI · OpenAlex · Extracted main text
We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is analogous to the standard definition of the average direct effect, and can be expressed without needing to compare outcomes across multiple randomized experiments. We show that the proposed indirect effect satisfies a decomposition theorem whereby, in a Bernoulli trial, the sum of the average direct and indirect effects always corresponds to the effect of a policy intervention that infinitesimally increases treatment probabilities. We also consider a number of parametric models for interference, and find that our non-parametric indirect effect remains a natural estimand when re-expressed in the context of these models.
appendix boundary found by none_found · 100% 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 | Sävje, F., Aronow, P. M. & Hudgens, M. G (2021) Average treatment effects in the presence of unknown interference | 1.000 | 7 | 4 | 100% |
| 2 | Leung, M. P (2020) Treatment and spillover effects under network interference | 1.000 | 5 | 5 | 100% |
| 3 | Aronow, P. M. & Samii, C (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.928 | 4 | 4 | 100% |
| 4 | Cai, J., De Janvry, A. & Sadoulet, E (2015) Social networks and the decision to insure | 0.928 | 4 | 3 | 100% |
| 5 | Hudgens, M. G. & Halloran, M. E (2008) Toward causal inference with interference | 0.928 | 4 | 3 | 100% |
| 6 | Li, S. & Wager, S (2020) Random graph asymptotics for treatment effect estimation under network interference self | 0.928 | 4 | 3 | 100% |
| 7 | VanderWeele, T. J. & Tchetgen Tchetgen, E. J (2011) Effect partitioning under interference in two-stage randomized vaccine trials | 0.811 | 4 | 2 | 100% |
| 8 | Basse, G. W., Feller, A. & Toulis, P (2019) Randomization tests of causal effects under interference | 0.737 | 3 | 2 | 100% |
| 9 | Bakshy, E., Rosenn, I., Marlow, C. & Adamic, L (2012) The role of social networks in information diffusion | 0.644 | 2 | 2 | 100% |
| 10 | Halloran, M. E. & Struchiner, C. J (1995) Causal inference in infectious diseases | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 29 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.