Heterogeneous Treatment Effect Estimation through Deep Learning

• Activity Number: 487 - Neural Networks, Deep Learning, and RKHS
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Ran Chen* and Hanzhong Liu
• Companies: Wharton and Center for Statistical Science, Tsinghua University
• Keywords: Heterogeneous Treatment Effect; Causal Inference; Machine Learning; Deep Learning; Neural Networks

Abstract

Estimating heterogeneous treatment effect is an important task in causal inference with wide application fields. It has also attracted increasing attention from machine learning community in recent years. In this work, we reinterpret the heterogeneous treatment effect estimation and propose ways to borrow strength from neural networks. We analyze the strengths and drawbacks of integrating neural networks into heterogeneous treatment effect estimation and clarify the aspects that need to be taken into consideration when designing a specific network. We proposed a specific network under our guidelines. In simulations, we show that our network performs better when the structure of data is complex, and reach a draw under the cases where other methods could be proved to be optimal.