The Blessings of Federation for Casual Inference

The Blessings of Federation for Casual Inference

Larry Han, PhD Candidate 

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. In this talk, I will discuss a novel Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source site information and avoid negative transfer, I will introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. FACE is communication-efficient and privacy-preserving, allowing participating sites to only share summary statistics once with other sites. I will show both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study ofBNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. I will also discuss a few extensions of FACE, namely 1) incorporating transfer learning to estimate treatment effects for underrepresented populations, 2) identifying subgroups and providing inference for heterogeneous treatment effects using federated trees, and 3) making robust inference for a prevailing model when no target outcome data exists.

Larry Han is a PhD candidate in Biostatistics at Harvard University, advised by Professor Tianxi Cai. His research focuses on developing novel statistical and machine learning methods to leverage real world data to improve decision-making in public health and clinical medicine. He received an AM in Biostatistics from Harvard, an MPhil in Healthcare Operations from the University of Cambridge, an MA in Global Affairs from Tsinghua University, and a BS in Biostatistics from the University of North Carolina at Chapel Hill.