Balancing Method for High Dimensional Causal Inference

TL;DR

This paper introduces a new framework for estimating individual treatment effects in high-dimensional observational data, offering a simpler yet effective alternative to existing methods with strong theoretical and empirical support.

Contribution

It proposes a novel balancing method for high-dimensional causal inference that is easier to implement and outperforms current state-of-the-art approaches.

Findings

Outperforms existing methods in most settings

Simpler and easier to implement

Provides both theoretical and empirical validation

Abstract

Causal inference has received great attention across different fields from economics, statistics, education, medicine, to machine learning. Within this area, inferring causal effects at individual level in observational studies has become an important task, especially in high dimensional settings. In this paper, we propose a framework for estimating Individualized Treatment Effects in high-dimensional non-experimental data. We provide both theoretical and empirical justifications, the latter by comparing our framework with current best-performing methods. Our proposed framework rivals the state-of-the-art methods in most settings and even outperforms them while being much simpler and easier to implement.