arXiv 2 Sep 2026 · Statistics — Methodology
arXiv:2609.02756 · PDF · Extracted main text
In the presence of interference, where the treatment assigned to one unit can affect the outcomes of others, many causal estimands depend on the treatment-assignment policy under which the experiment is conducted. This policy dependence creates a fundamental challenge for off-policy estimation, where the goal is to estimate causal quantities under a hypothetical intervention policy different from the one used to collect data. We study this problem of off-policy estimation of causal effects for heterogeneous Bernoulli policies. By representing exposure-weighted potential outcomes in the biased Fourier basis of the experimental design, we construct, for any prespecified Fourier subspace encoding the assumed interference structure, the unique minimum-$L^2$ weight that transports every function in that subspace. Global and local inverse-probability weights, linear-interference weights, and no-interference weights are special cases. The weight variance is a structured chi-square distance between the experiment and target policies. When the assumed interference structure is misspecified, the introduced bias couples the omitted outcome spectrum with the corresponding policy-shift coefficients, yielding a sharp robustness bound and a bias-variance trade-off. A Fourier-neighborhood-overlap condition gives consistency under structured interference, and we state a Doob-martingale central limit theorem for off-policy estimators. As the variance is not identified, we derive identifiable bounds and associated conservative estimators of the variance. Simulations illustrate these theoretical results for the design and analysis of experiments under network interference and design mismatch.
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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 | O’Donnell, Ryan (2014) Analysis of Boolean Functions | 0.737 | 3 | 2 | 100% |
| 2 | Hu, Yuchen and Li, Shuangning and Wager, Stefan (2022) Average direct and indirect causal effects under interference | 0.644 | 2 | 2 | 100% |
| 3 | Jerzy Neyman (1990) On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 | 0.644 | 2 | 2 | 100% |
| 4 | Fredrik Sävje and Peter M. Aronow and Michael G. Hudgens (2021) Average treatment effects in the presence of unknown interference | 0.644 | 2 | 2 | 100% |
| 5 | Sävje, F (2023) Causal inference with misspecified exposure mappings: separating definitions and assumptions | 0.644 | 2 | 2 | 100% |
| 6 | Peter M. Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.405 | 1 | 1 | 100% |
| 7 | Chin, Alex and Eckles, Dean and Ugander, Johan (2022) Evaluating stochastic seeding strategies in networks self | 0.405 | 1 | 1 | 100% |
| 8 | Cortez, Mayleen and Eichhorn, Matthew and Yu, Christina (2022) Staggered rollout designs enable causal inference under interference without network knowledge | 0.405 | 1 | 1 | 100% |
| 9 | Crépon, Bruno and Duflo, Esther and Gurgand, Marc and Rathelot, Rola… (2013) Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment * | 0.405 | 1 | 1 | 100% |
| 10 | Deville, Jean-Claude and Särndal, Carl-Erik (1992) Calibration Estimators in Survey Sampling | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.