Estimating causal effects under interference using Bayesian generalized propensity scores


Forastiere, Laura; Mealli, Fabrizia; Wu, Albert; & Airoldi, Edoardo M. (2018). Estimating causal effects under interference using Bayesian generalized propensity scores. vol. 1807.11038


In most real-world systems units are interconnected and can be represented as networks consisting of nodes and edges. For instance, in social systems individuals can have social ties, family or financial relationships. In settings where some units are exposed to a treatment and its effect spills over connected units, estimating both the direct effect of the treatment and spillover effects presents several challenges. First, assumptions on the way and the extent to which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment is not under the control of the investigator, confounding and homophily are potential threats to the identification and estimation of causal effects on networks. Here, we make two structural assumptions: i) neighborhood interference, which assumes interference operates only through a function of the immediate neighbors’ treatments ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for treatment and spillover effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors’ treatment. Adjustment for propensity score is performed using a penalized spline regression. Inference capitalizes on a three-step Bayesian procedure which allows to take into account the uncertainty in the propensity score estimation and avoiding model feedback. Finally, correlation of interacting units is taken into account using a community detection algorithm and incorporating random effects in the outcome model. All these sources of variability, including variability of treatment assignment, are accounted for in the posterior distribution of finite-sample causal estimands. We conducted a simulation study where we assess the performance of our estimator on different type of networks, generated from a stochastic block model and a latent space model or given from the friendship-network of the Add-Health study.



Causal inference Interference Spillovers Bayesian interference

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Journal Article

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Forastiere, Laura
Mealli, Fabrizia
Wu, Albert
Airoldi, Edoardo M.

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