A Semiparametric Perspective on Unsupervised Domain Adaptation

Music City Center 

In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q. In this talk, we consider the unsupervised domain adaptation setting under the label shift assumption. We estimate a parameter of interest in population Q by leveraging information from P, where three ingredients are essential: (a) the common conditional distribution of X given Y, (b) the regression model of Y given X in P, and (c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the model misspecifications of both (b) and (c). We rigorously study the theoretical properties of our proposed methods. Empirically, we illustrate our proposed methods in the MIMIC-III database.

Domain adaptation, Semiparametric