Estimating Average Causal Effects Under General Interference

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Making Decisions that Reduce Discriminatory Impacts (2019, unknown): “As in Aronow & Samii (2017), we are not concerned about direct causal connections between different impacts {Y (i), Y (j)}.”
Handbook of Big Data (2016, methodology): “Aronow and Samii (2013) proposed a method for estimating unit-level and spillover effects of randomized treatments in relatively sparse networks. Their method is based on an exposure model, similar to the model H (yi(z);β, τ) = yi(0) of Bowers et al. (2013), and the exposure conditions of Ugander et”
Causal inference with misspecified exposure mappings: separating definitions and assumptions (2021, methodology): “The analysis of this estimator can be extended to several other estimators, including the estimators discussed by Aronow and Samii (2017) and Karwa and Airoldi (2018).”
Estimating peer effects in networks with peer encouragement designs (2016, methodology): “In SI Appendix , we combine this assumption with a specific model of local interference (50, 51) to conduct analyses quantifying the sensitivity of our results to nuisance interference.”
Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference (2017, methodology): “This reduces the dimension of the so-called exposure mapping (Aronow and Samii, 2012), i.e., the dimension-reducing function of all subjects’ exposures upon which the potential outcomes are assumed to depend, from n to two, recovering tractable estimation.”