Safe and Robust Causal Inference for High-Dimensional Complex Data

Cornell University, Ithaca NY

Investigators

Abstract

Understanding causal effects among multivariate variables is central to many empirical and experimental types of research. In an era where data is vast, statisticians are faced with the challenge of drawing causal inferences from massive data. For example, the analysis of data can be complicated by the presence of potentially confounding variables, heterogeneity, and temporal dependence within populations. This poses tremendous computational and statistical challenges to existing causal inference methods. Driven by the availability of modern datasets, the research project aims to develop cutting-edge machine-learning methods to address the theoretical, methodological, and computational challenges of drawing causal inferences from massive data. The development of the proposed research would not only push the frontier of causal inference theory but also benefit a broad range of researchers in a variety of areas, including medicine, epidemiology, computer science, and social science. This project also provides research training opportunities for graduate students. The goal of the project is to develop a novel high-dimensional causal inference framework that is influence-function doubly-robust and safe relative to a class of base estimators. Specific projects include proposing a covariate balancing methodology coupled with modern machine learning techniques for efficient causal inference with high-dimensional data, optimally distributed covariate balancing for massive heterogeneous data, and a sequential covariate balancing approach for marginal structural models in longitudinal data. A common theme throughout is the use of the covariate balancing methodology, which comes from the ``propensity score tautology,” the estimated propensity score is appropriate if it balances the covariates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.