DRTCI: Learning Disentangled Representations for Temporal Causal Inference
This work addresses the challenge of handling time-varying confounders in medical decision-making, representing an incremental improvement over existing methods by adapting static disentanglement ideas to the temporal setting.
The paper tackles the problem of isolating selection bias in temporal causal inference for medical treatment evaluation by disentangling covariate representations into factors that influence treatment, outcome, or both, rather than balancing all covariates as in prior methods.
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.