arXiv 24 Aug 2026 · Statistics — Methodology
arXiv:2608.22890 · PDF · Extracted main text
Analysis of experimental data becomes challenging when the underlying population is connected by a network. Exposure mapping is a common tool in the literature for defining and estimating spillover effects. These mappings reduce the dimensionality of the estimand, thereby facilitating identifiability. It is assumed that this mapping is correctly specified, leaving the choice of the exposure mapping to the analyst. This makes estimators of the spillover effect, such as the Horvitz-Thompson estimator, vulnerable to bias from model misspecification. Although these estimators have been shown to be robust to certain forms of controlled misspecification, there has been relatively little methodological progress in empirically investigating appropriate exposure mappings. In this paper, we propose a novel design-based model specification framework for causal inference. Building on this, we develop a randomization-testing procedure to assess the correct specification of an exposure-mapping model in the presence of network interference. We provide theoretical guarantees for the asymptotic validity of the proposed testing procedure. We establish the favorable power properties of our method through an extensive simulation study and illustrate it in a field experiment investigating the effect of anti-conflict norms among adolescents.
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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 | Paluck, Elizabeth Levy and Shepherd, Hana and Aronow, Peter M (2016) Changing climates of conflict: A social network experiment in 56 schools | 1.000 | 6 | 3 | 100% |
| 2 | Sävje, Fredrik (2024) Causal inference with misspecified exposure mappings: separating definitions and assumptions | 0.874 | 5 | 2 | 100% |
| 3 | Zhang, Yao and Zhao, Qingyuan (2025) Multiple conditional randomization tests for lagged and spillover treatment effects | 0.843 | 4 | 4 | 75% |
| 4 | Aronow, Peter M and Samii, Cyrus (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.843 | 3 | 3 | 100% |
| 5 | Lehmann, Erich Leo and Romano, Joseph P (2005) Testing statistical hypotheses | 0.843 | 3 | 3 | 100% |
| 6 | Zhong, Liang (2024) Unconditional randomization tests for interference | 0.737 | 4 | 3 | 50% |
| 7 | Gao, Chao and Harshaw, Christopher and Sävje, Fredrik and Wang, Yitan (2026) On the Impossibility of Specification Testing of Interference Models Based on Exposure Mappings | 0.737 | 3 | 2 | 100% |
| 8 | Athey, Susan and Eckles, Dean and Imbens, Guido W (2018) Exact p-values for network interference | 0.644 | 2 | 2 | 100% |
| 9 | Basse, Guillaume W and Feller, Avi and Toulis, Panos (2019) Randomization tests of causal effects under interference | 0.644 | 2 | 2 | 100% |
| 10 | Cai, Jing and Janvry, Alain De and Sadoulet, Elisabeth (2015) Social networks and the decision to insure | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.