  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.
