Learning Representations for Counterfactual Inference
This addresses the need for accurate causal inference in fields like healthcare, education, and ecology, where observational data is abundant but traditional methods may be limited.
The paper tackles the problem of answering counterfactual questions from observational data, such as predicting patient outcomes under different treatments, and proposes a deep learning algorithm that significantly outperforms the previous state-of-the-art.
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.