Speaker: Kwahangho Kim

Abstract: We develop Causal Clustering, a new framework for the analysis of treatment effect heterogeneity by leveraging tools in clustering analysis. We pursue an efficient way of ascertaining subgroups with similar treatment effects - viewing each of them as a separate cluster - by harnessing widely-used clustering methods. We show that k-means, density-based, hierarchical clustering algorithms can be successfully adopted into our framework, almost only at an additional cost of estimating nuisance regression functions for the outcome process. Particularly for k-means causal clustering, we develop an efficient nonparametric estimator that attains fast convergence rates under weak nonparametric conditions on the nuisance function estimation, and find the conditions that assure asymptotic normality of the cluster centers. Our framework can be extended to outcome-wide studies where we assess treatment effects over numerous outcomes.

This is joint work with Edward, Jisu and Larry. In this talk, I will focus more on the idea (i.e. how the two different concepts - clustering and causal inference - can be harmonized together), and how the semi-parametric and SML theories can help to develop more appealing estimators.