Speaker：Zhongyi Zhu (Fudan University)

Time：2024-12-30, 15:00

Location：Conference Room S107 at Experiment Building at Haiyun Campus

Abstract:

With the increasing availability of datasets, developing data fusion methods to leverage the strengths of different types of data to draw causal effects is of great practical importance to many scientific fields. In this paper, we consider estimating the quantile treatment effects with small validation data with fully-observed confounders and large auxiliary data with unmeasured confounders. We propose a fused quantile treatment effects estimator (FQTE) by integrating the information from two datasets based on doubly robust estimating functions. We allow for the misspecification of the models on the dataset with unmeasured confounders. Under mild conditions, we show that the proposed FQTE is asymptotically normal and more efficient than the initial QTE estimator using the validation data solely. By establishing the asymptotic linear forms of related estimators, convenient methods for covariance estimation are provided to make our method easy to implement. Simulation studies demonstrate the empirical validity and improved efficiency of our fused estimators. We illustrate the proposed method with an application.