Learning Representations for Counterfactual Inference

TL;DR

This paper introduces a deep learning framework for counterfactual inference from observational data, combining domain adaptation and representation learning, and demonstrates its superior performance over previous methods.

Contribution

It presents a novel algorithmic approach that integrates ideas from domain adaptation and representation learning for counterfactual inference, with theoretical and empirical validation.

Findings

Deep learning algorithm outperforms previous state-of-the-art methods.

The approach provides a theoretical justification for counterfactual inference.

Empirical results show significant improvements in accuracy.

Abstract

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.