[Statlist] ETH/UZH Research Seminar on Statistics by Xinwei Shen, University of Washington -- ETH Zurich, 11 December 2025

[Maurer Letizia]

We are glad to announce and invite you to the following talk in the ETH/UZH Research Seminar on Statistics:

„Distributional Causal Inference: from Estimation to Simulation“

by Xinwei Shen, University of Washington

Date and Time: Thursday, 11 December 2025 at 16.15 h
Place: ETH Zurich, HG E 5

Abstract: Classical causal inference typically targets low-dimensional estimands such as the average treatment effect. A richer understanding, however, requires characterizing the full outcome distribution under different treatments. In addition, the ability to simulate counterfactual outcomes is essential for causal model selection and evaluation. Recent advances in distributional learning provide a principled foundation for these goals. In this talk, we build on engression—a distributional learning approach—to develop methods for estimating distributional causal effects and generating data from causal models. We first introduce a distributional method for instrumental variable settings with unobserved confounders, enabling estimation of full interventional distributions from which classical estimands arise as functionals. When additional covariates are observed but marginal causal effects remain the central interest, as is common in clinical trials, we propose a framework that parametrizes the joint observed distribution around the causal margin with no redundancy. This allows for both estimation and simulation under user-specified interventions.
Joint work with Anastasiia Holovchak, Sorawit Saengkyongam, Nicolai Meinshausen, Linying Yang, and Robin Evans

This is joint work with Juraj Marusic and Cindy Rush

Seminar website, https://math.ethz.ch/sfs/news-and-events/research-seminar.html


Organisers:  A. Bandeira, P. Bühlmann, Y. Chen, R. Furrer, L. Held, T. Hothorn, D. Kozbur,  J. Peters,  M. Wolf, J. Ziegel
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