Webcast option: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5d4cd3f3-1421-46c1-8888-b34d0108624b
Subhabrata Sen
Causal effect estimation under interference using mean field methods
We will discuss causal effect estimation from observational data under interference. We adopt the chain-graph formalism of Tchetgen-Tchetgen et. al. (2021). Under “mean-field” assumptions on the interaction networks, we will introduce novel algorithms for causal effect estimation using Naive Mean Field approximations and Approximate Message Passing. Our algorithms are provably consistent under a “high-temperature” assumption on the underlying model. Finally, we will discuss parameter estimation in these models using maximum pseudo-likelihood, and establish the consistency of the downstream plug-in estimator.
Based on joint work with Sohom Bhattacharya (U Florida).
3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13 floor, there will be light snacks and beverages in the kitchen area. For more details and upcoming events visit our website at https://statistics.yale.edu/calendar.