Figure 5. Recommender systems could learn from retrospective data to assist in clinical decision-making.

Logged healthcare data comprises multimodal patient contexts x, interventions y based on the standard of care (π₀), and feedback δ based on the outcome of the intervention. Learning from such data is challenging because of the lack of two-arm design and the biased data based on the changing standard of care. Counterfactual recommender systems learn theoretically guaranteed unbiased policies from these data. Then, the validated policy π can be applied prospectively to support physicians’ management decisions.