B1369
Title: DeepMed: Semiparametric causal mediation analysis with debiased deep learning
Authors: Zhonghua Liu - Columbia University (United States) [presenting]
Abstract: Causal mediation analysis can unpack the black box of causality and is, therefore, a powerful tool for disentangling causal pathways in biomedical and social sciences and evaluating machine learning fairness. To reduce bias for estimating natural direct and indirect effects in mediation analysis, a new method is proposed called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. Novel theoretical results are obtained that the DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support findings and expose the gap between theory and practice. As a proof of concept, DeepMed is applied to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.
