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

Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck