Julius Owusu, Monika Avila Márquez
arXiv 24 Apr 2026 · Econometrics
arXiv:2604.22532 · PDF · DOI · OpenAlex · Extracted main text
Empirical researchers routinely invoke the no-interference or individualistic treatment response (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary interference is present. We show that, under restrictions on dependence between treatment assignments across units, conventional ITR-based identification formulas -- including those underlying selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences -- identify well-defined causal objects: types of average direct effects (ADEs). These results do not require knowledge of the interference structure or specification of exposure mappings. We also propose a sensitivity analysis framework that quantifies the robustness of statistical inference to violations of treatment-assignment independence under arbitrary 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 | Tchetgen, Eric J Tchetgen and VanderWeele, Tyler J (2012) On causal inference in the presence of interference | 1.000 | 7 | 4 | 100% |
| 2 | Imbens, Guido W and Rubin, Donald B (2015) Causal inference in statistics, social, and biomedical sciences | 1.000 | 5 | 3 | 100% |
| 3 | LaLonde, Robert J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.928 | 5 | 3 | 80% |
| 4 | Xu, Ruonan (2023) Difference-in-differences with interference: A finite population perspective | 0.843 | 3 | 3 | 100% |
| 5 | Rosenbaum, Paul R (2002) Observational studies | 0.737 | 3 | 2 | 100% |
| 6 | Forastiere, Laura and Airoldi, Edoardo M and Mealli, Fabrizia (2020) Identification and estimation of treatment and interference effects in observational studies on networks | 0.693 | 5 | 1 | 100% |
| 7 | Angrist, Joshua D and Pischke, Jörn-Steffen (2009) Mostly harmless econometrics: An empiricist's companion | 0.644 | 2 | 2 | 100% |
| 8 | Aronow, Peter M and Samii, Cyrus and others (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.644 | 2 | 2 | 100% |
| 9 | Aronow, Peter M and Basta, Nicole E and Halloran, M Elizabeth (2017) The regression discontinuity design under interference: A local randomization-based approach | 0.644 | 2 | 2 | 100% |
| 10 | Forastiere, Laura and Airoldi, Edoardo M and Mealli, Fabrizia (2021) Identification and estimation of treatment and interference effects in observational studies on networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.