Sequential Counterfactual Decision-Making Under Confounded Reward

This addresses a methodological challenge in reinforcement learning and causal inference for researchers, but appears incremental as it builds on existing counterfactual frameworks.

The paper tackles the problem of sequential decision-making when rewards are confounded, by formalizing a counterfactual policy-space that incorporates the agent's natural predilection through soft interventions.