Events
Statistics & Data Science Seminar
"Causal Effect Estimation Under Interference Using Mean Field Methods"
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Speaker: Subhabrata Sen Department of Statistics at Harvard University Harvard University Monday, October 13, 2025 4:00PM - 5:00PM Teatime at 3:30pm in 1307
Talk at 4:00pm in 1327 Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5d4cd3f3-1421-46c1-8888-b34d0108624b |
Abstract: 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).
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- Statistics & Data Science Seminar
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