Colloquium Series: Alex Luedtke

Our upcoming event for the Statistics Colloquium Series is scheduled for Monday, September 21st from 12:00 – 1:00pm (ET) and will be an in-person presentation at Maxwell-Dworkin, Room 119. Lunch will be provided to guests following the talk. This week's speaker will be Alex Luedtke of Professor of Health Care Policy and Affiliate in Statistics at Harvard University.

Sinkhorn Treatment Effects: A Causal Optimal Transport Measure

Abstract: We introduce the Sinkhorn treatment effect, an entropic optimal transport measure of divergence between counterfactual distributions. Unlike classical quantities such as the average treatment effect, this measure captures differences across entire distributions. We analyze this divergence as a statistical functional and show it can be written as a smooth transformation of counterfactual mean embeddings with an appropriate kernel. This characterization allows us to establish first-order pathwise differentiability in general, and second-order pathwise differentiability under the null hypothesis of equal counterfactual distributions. Leveraging this smoothness, we construct debiased estimators and use them to obtain asymptotically valid tests for distributional treatment effects with a fixed entropic regularization parameter. Because the power of the test depends on this unknown parameter, we further propose an aggregated test that combines evidence across a grid of regularization choices. Experiments on simulated and image data demonstrate the practical advantages of our estimator and testing procedure.

Speakers Bio:

Alex Luedtke is a Professor of Health Care Policy and Affiliate in Statistics at Harvard University. Previously, he held faculty appointments in Statistics and Biostatistics at the University of Washington and Fred Hutch.

Alex works at the intersection of semiparametrics, causal inference, and machine learning. Much of his work leverages computational power to automate the construction of efficient statistical procedures. To this end, he has developed methods that let machines "learn to learn" from data, and algorithms that work out derivations once done by hand. He applies these tools as part of multidisciplinary teams to improve decision-making in mental health and infectious disease research.

Selected honors include a COPSS Emerging Leader Award, NIH Director's New Innovator Award, Mortimer Spiegelman Award, and AWS Machine Learning Research Award. His work has been supported by NIH, NSF, PCORI, WHO, and Netflix.

As a mentor, Alex has supervised the completed dissertations of 11 PhD students in statistics and biostatistics.

Alex received a PhD in Biostatistics from UC Berkeley and an ScB in Applied Mathematics from Brown University.