# Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck

## TL;DR

This paper introduces a causal information bottleneck approach leveraging the IB principle to improve the reliability of individual treatment effect estimation, achieving state-of-the-art results with uncertainty quantification.

## Contribution

It proposes a novel causal information bottleneck framework with regularization for more reliable ITE estimation, incorporating independence constraints and semi-supervised learning.

## Key findings

- Achieves state-of-the-art ITE estimation accuracy.
- Provides more reliable predictions with uncertainty estimates.
- Demonstrates effectiveness on real-world datasets.

## Abstract

Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and is commonly considered in diverse mission-critical applications. In this paper, we propose an information theoretic approach in order to find more reliable representations for estimating ITE. We leverage the Information Bottleneck (IB) principle, which addresses the trade-off between conciseness and predictive power of representation. With the introduction of an extended graphical model for causal information bottleneck, we encourage the independence between the learned representation and the treatment type. We also introduce an additional form of a regularizer from the perspective of understanding ITE in the semi-supervised learning framework to ensure more reliable representations. Experimental results show that our model achieves the state-of-the-art results and exhibits more reliable prediction performances with uncertainty information on real-world datasets.