Adversarial balancing-based representation learning for causal effect inference with observational data

Xin Du (Corresponding author), Lei Sun, Wouter Duivesteijn, Alexander Nikolaev, Mykola Pechenizkiy

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
43 Downloads (Pure)


Learning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. 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, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.

Original languageEnglish
Pages (from-to)1713-1738
Number of pages26
JournalData Mining and Knowledge Discovery
Issue number4
Publication statusPublished - Jul 2021


  • Causal inference
  • Observational study General Adversarial Networks
  • Representation learning


Dive into the research topics of 'Adversarial balancing-based representation learning for causal effect inference with observational data'. Together they form a unique fingerprint.

Cite this