Factual Observation Based Heterogeneity Learning for Counterfactual Prediction
Extant causal methods exclusively exploit the heterogeneity based on the observed covariates for heterogeneous outcome prediction. Even with nowadays big data, the collected covariates may not contain complete confounders. When some confounders are absent, the methods can suffer from confounding bias and missing heterogeneity. To address these two issues, we propose to leverage the factual observation in the observational data to recover the latent confounders. Since the learned confounder representation exploits the heterogeneity of latent confounders, it leads to finer granular heterogeneous outcome prediction, which is closer to the individual-level than prediction conditional on only covariates. Specifically, we propose a novel Factual Observation based Heterogeneity Learning (FOHL) algorithm with an encoder for confounder representation learning and a decoder for outcome prediction. Theoretical analysis reveals the validity of recovering confounders from factual observations to make the heterogeneous prediction closer to the individual-level. Furthermore, experimental results demonstrate that our FOHL method can outperform the existing baselines.
