Online Program Home
My Program

Abstract Details

Activity Number: 391 - Leveraging Disparate Sources of Data and Machine Learning to Improve Causal Inference
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #307101
Title: Transfer Learning for Estimating Causal Effects Using Neural Networks
Author(s): Soeren Kuenzel* and Jasjeet Sekhon and Bradly Reinhold Stadie and Nikita Vemuri
Companies: and UC Berkeley and UC Berkeley and UC Berkeley
Keywords: Causal Inference; Randomized Controlled Trials; Transfer Learning; Heterogeneous Treatment Effects; Conditional Average Treatment Effect

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program