DRTCI: Learning Disentangled Representations for Temporal Causal Inference. (arXiv:2201.08137v1 [cs.LG])

Medical professionals evaluating alternative treatment plans for a patient
often encounter time varying confounders, or covariates that affect both the
future treatment assignment and the patient outcome. The recently proposed
Counterfactual Recurrent Network (CRN) accounts for time varying confounders by
using adversarial training to balance recurrent historical representations of
patient data. However, this work assumes that all time varying covariates are
confounding and thus attempts to balance the full state representation. Given
that the actual subset of covariates that may …

arxiv causal inference learning