  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 in fact be confounding is in
general unknown, recent work on counterfactual evaluation in the static,
non-temporal setting has suggested that disentangling the covariate
representation into separate factors, where each either influence treatment
selection, patient outcome or both can help isolate selection bias and restrict
balancing efforts to factors that influence outcome, allowing the remaining
factors which predict treatment without needlessly being balanced.
