Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck

7 Jun 2019 Sungyub Kim Yongsu Baek Sung Ju Hwang Eunho Yang

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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Causal Inference IDHP OLS with separate regressors for each treatment Average Treatment Effect Error 0.31 # 3

Methods used in the Paper


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