Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

2 code implementations • 30 Apr 2019 • Xin Du, Lei Sun, Wouter Duivesteijn, Alexander Nikolaev, Mykola Pechenizkiy

The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.

Causal Inference Representation Learning +2