DRTCI: Learning Disentangled Representations for Temporal Causal Inference

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. We hypothesize that such disentanglement should be possible in the temporal setting as well, and would be beneficial when dealing with time varying confounders. We propose DRTCI, a model for temporal causal inference which uses a recurrent neural network to learn hidden representation of the patient's evolving covariates that disentangles into three factors that each causally determine either treatment, outcome or both treatment and outcome. The model is evaluated on the same simulated model of tumour growth used to evaluate the CRN, with varying degrees of time-dependent confounding. The resulting outcome predictions from DRTCI significantly outperform the predictions from existing baselines especially for cases with high confounding and minimal historical data (early prediction). Ablation experiments are additionally performed to identify the key contributing factors to the performance of DRTCI.