Heterogeneous Treatment Effect Estimation through Deep Learning

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.

References in corpus (7)

• Recursive Partitioning for Heterogeneous Causal Effects
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
• Estimating treatment effect heterogeneity in randomized program evaluation
• Lasso adjustments of treatment effect estimates in randomized experiments
• Transfer Learning for Estimating Causal Effects using Neural Networks
• A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform