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Identifying Treatment and Spillover Effects Using Exposure Contrasts

Michael P. Leung

arXiv 13 Mar 2024 · Econometrics

arXiv:2403.08183 · PDF · DOI · OpenAlex · Extracted main text

Abstract

To report spillover effects, a common practice is to regress outcomes on statistics capturing treatment variation among neighboring units. This paper studies the causal interpretation of nonparametric analogs of these estimands, which we refer to as exposure contrasts. We demonstrate that their signs can be inconsistent with those of the unit-level effects of interest even under unconfounded assignment. We then provide interpretable restrictions under which exposure contrasts are sign preserving and therefore have causal interpretations. We discuss the implications of our results for cluster-randomized trials, network experiments, and observational settings with peer effects in selection into treatment.

Citation extraction

62
references
100
in-text mentions
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distinct cited
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self-citations
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Leung and Loupos (2025) Graph Neural Networks for Causal Inference Under Network Confounding1.00074100%
2Mele (2017) A Structural Model of Dense Network Formation0.9416383%
3Leung (2022) Causal Inference Under Approximate Neighborhood Interference self0.92843100%
4Vazquez-Bare (2023) Identification and Estimation of Spillover Effects in Randomized Experiments0.73732100%
5Sobel (2006) What Do Randomized Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference0.6443267%
6Aronow and Samii (2017) Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment0.64422100%
7Auerbach, Auerbach and Tabord-Meehan (2024) Discussion of `Causal inference with misspecified exposure mappings: separating definitions and assumptions'0.64422100%
8Cai, De Janvry and Sadoulet (2015) Social Networks and the Decision to Insure0.64422100%
9Leung (2025) Cluster-Randomized Designs with Cross-Cluster Interference self0.64422100%
10Lu, Wang and Zhu (2019) Place-Based Policies, Creation, and Agglomeration Economies: Evidence from China's Economic Zone Program0.64422100%

Showing the top 10 of 62 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Decomposition of Spillover Effects Under Misspecification: Pseudo-True Estimands and a Local-Global Extension0.84343
2Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico0.64422
3Graph Neural Networks for Causal Inference Under Network Confounding0.51121
4Evaluating Policy Effects under Network Interference without Network Information: A Transfer Learning Approach0.51121
5Cluster-Randomized Trials with Cross-Cluster Interference0.40511