arXiv 12 Feb 2026 · Econometrics
arXiv:2602.12023 · PDF · DOI · OpenAlex · Extracted main text
Applied work with interference typically models outcomes as functions of own treatment and a low-dimensional exposure mapping of others' treatments, even when that mapping may be misspecified. This raises a basic question: what policy object are exposure-based estimands implicitly targeting, and how should we interpret their direct and spillover components relative to the underlying policy question? We take as primitive the marginal policy effect, defined as the effect of a small change in the treatment probability under the actual experimental design, and show that any researcher-chosen exposure mapping induces a unique pseudo-true outcome model. This model is the best approximation to the underlying potential outcomes that depends only on the user-chosen exposure. Utilizing that representation, the marginal policy effect admits a canonical decomposition into exposure-based direct and spillover effects, and each component provides its optimal approximation to the corresponding oracle objects that would be available if interference were fully known. We then focus on a setting that nests important empirical and theoretical applications in which both local network spillovers and global spillovers, such as market equilibrium, operate. There, the marginal policy effect further decomposes asymptotically into direct, local, and global channels. An important implication is that many existing methods are more robust than previously understood once we reinterpret their targets as channel-specific components of this pseudo-true policy estimand. Simulations and a semi-synthetic experiment calibrated to a large cash-transfer experiment show that these components can be recovered in realistic experimental designs.
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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 | Egger, Dennis and Haushofer, Johannes and Miguel, Edward and Niehaus… General Equilibrium Effects of Cash Transfers: Experimental Evidence from Kenya | 1.000 | 8 | 3 | 100% |
| 2 | Aronow, Peter M. and Samii, Cyrus Estimating Average Causal Effects under General Interference, with Application to a Social Network Experiment | 1.000 | 6 | 3 | 100% |
| 3 | Hudgens, Michael G. and Halloran, M. Elizabeth Toward Causal Inference with Interference | 1.000 | 5 | 3 | 100% |
| 4 | Filmer, Deon and Friedman, Jed and Kandpal, Eeshani and Onishi, Junko Cash transfers, food prices, and nutrition impacts on ineligible children | 0.950 | 7 | 4 | 86% |
| 5 | Arkhangelsky, Dmitry and Rutgers, Wisse Evaluating Local Policies in Centralized Markets | 0.928 | 4 | 3 | 100% |
| 6 | Cai, Jing and Janvry, Alain De and Sadoulet, Elisabeth Social networks and the decision to insure | 0.928 | 4 | 3 | 100% |
| 7 | Sävje, Fredrik (2024) Causal inference with misspecified exposure mappings: separating definitions and assumptions | 0.928 | 4 | 3 | 100% |
| 8 | Hu, Yuchen and Li, Shuangning and Wager, Stefan Average direct and indirect causal effects under interference | 0.874 | 7 | 2 | 100% |
| 9 | Munro, Evan and Kuang, Xu and Wager, Stefan (2025) Treatment effects in market equilibrium | 0.870 | 38 | 10 | 66% |
| 10 | Li, Shuangning and Wager, Stefan Random graph asymptotics for treatment effect estimation under network interference | 0.853 | 29 | 8 | 62% |
Showing the top 10 of 66 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers | 0.405 | 1 | 1 |