This seminar features a paper presentation by Laura Forastiere (Yale School of Public Health).
Interventions in connected populations often exhibit complex dynamics, where effects travel along existing social ties, alter the ties themselves, and propagate through these network changes. This talk presents weighting estimators to evaluate causal effects under static and dynamic network interference by conceptualizing the network across three distinct roles: as a channel, an outcome, and a mediator.
First, to understand how far treatment effects travel through the network as a channel, we propose a framework for estimating higher-order spillover effects under partial interference. By defining distance-specific hypothetical assignments, we develop Horvitz-Thompson, Hájek, and weighted least squares estimators that do not require an analyst-specified exposure mapping, thereby protecting against common misspecification biases. Second, recognizing that interventions can actively rewire social connections and act as an outcome, we construct a dyad-level causal framework to evaluate effects on the ties themselves. This approach defines direct, spillover, and total effects to quantify how interventions impact the formation of new ties and the retention or dissolution of existing ones.
Finally, bridging traditional interference and mediation analyses, we conceptualize dyadic ties as mediators and define mediated and non-mediated direct, spillover, and ego-alter effects under a hypothetical Bernoulli treatment allocation. For observational clustered data, we construct multiply robust estimators that combine an outcome model, a dyadic mediator model, and a cluster-level propensity score, achieving consistency whenever any two of the three models are correctly specified. These methodologies are validated through simulation studies and applied to two empirical settings. Using data from a maternal and child health trial in Honduras, we evaluate information diffusion and program-induced network rewiring. Additionally, applying our mediation framework to observational data from 75 villages in Karnataka, India, we quantify the extent to which the dissolution of informal ties mediated the effects of microfinance exposure on household borrowing.
Jointly with Qixiang Xu
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