Estimating individual treatment effect: generalization bounds and algorithms

TL;DR

This paper introduces new algorithms and theoretical bounds for estimating individual treatment effects from observational data, emphasizing balanced representations and distribution similarity to improve causal inference accuracy.

Contribution

It provides a novel theoretical analysis with generalization bounds and develops algorithms that learn balanced representations to enhance ITE estimation.

Findings

Algorithms match or outperform state-of-the-art methods.

Explicit bounds for Wasserstein and MMD distances.

Theoretical analysis links representation quality to estimation error.

Abstract

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.