Causal Effect Prediction with Flow-based Inference

Causal effect inference has many applications in data analysis and predictions, e.g., user behavior modeling, medical treatment effect prediction, etc. We introduce a new method to perform causal effect inference using flow-based latent-variable models. Our method leverages the expressive power of flow-based models and tries to recover the complex relationship between observations and unobserved confounders. A methodology has been developed to perform causal effect inference along with theoretical analysis. Experimental studies are presented to verify the proposed approach. Empirical results show that the proposed method outperforms baselines on different datasets.