Transfer Learning for Estimating Causal Effects Using Neural Networks

• 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
• 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

Abstract

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.