When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-disadvantaged subgroups. These biases tend to occur in multi-stage decision making and can be self-perpetuating, which if unaccounted for could cause serious unintended consequences that limit access to care or treatment benefit. Counterfactual fairness (CF) offers a promising statistical tool grounded in causal inference to formulate and study fairness. In this paper, we propose a general framework for fair sequential decision making. We theoretically characterize the optimal CF policy and prove its stationarity, which greatly simplifies the search for optimal CF policies by leveraging existing RL algorithms. The theory also motivates a sequential data preprocessing algorithm to achieve CF decision making under an additive noise assumption. We prove and then validate our policy learning approach in controlling unfairness and attaining optimal value through simulations. Analysis of a digital health dataset designed to reduce opioid misuse shows that our proposal greatly enhances fair access to counseling.

Chronic pain significantly diminishes the quality of life for millions worldwide. While psychoeducation and therapy can improve pain outcomes, many individuals experiencing pain lack access to evidence-based treatments or fail to complete the necessary number of sessions to achieve benefit. Reinforcement learning (RL) shows potential in tailoring personalized pain management interventions according to patients' individual needs while ensuring the efficient use of scarce clinical resources. However, clinicians, patients, and healthcare decision-makers are concerned that RL solutions could exacerbate disparities associated with patient characteristics like race or gender. In this article, we study gender fairness in personalized pain care recommendations using a real-world application of reinforcement learning (Piette et al., 2022a). Here, adhering to gender fairness translates to minimal or no disparity in the utility received by subpopulations as defined by gender. We investigate whether the selection of relevant patient information (referred to as features) used to assist decision-making affects gender fairness. Our experiments, conducted using real-world data (Piette, 2022), indicate that included features can impact gender fairness. Moreover, we propose an RL solution, NestedRecommendation, that demonstrates the ability: i) to adaptively learn to select the features that optimize for utility and fairness, and ii) to accelerate feature selection and in turn, improve pain care recommendations from early on, by leveraging clinicians' domain expertise.