Quotient DAGs for Off-Policy Evaluation:Forward-Flow Importance Sampling and Exact Slate Propensities
arXiv:2605.29500v1 Announce Type: new Abstract: Off-policy evaluation estimates how a target policy would perform using data collected by a different behavior policy, which is crucial when online testing is costly or risky, such as in recommendation or healthcare. Standard importance sampling reweights each logged trajectory, but it can treat details of the generation process as meaningful even when the evaluation target ignores them: for example, an autoregressive slate recommender may generate an ordered sequence of items while the reward and downstream estimator depend only on the unordered
The increasing complexity of AI systems, particularly in recommendation and reinforcement learning, necessitates more robust and efficient off-policy evaluation methods to de-risk deployment.
Improved off-policy evaluation allows for safer and more rapid iteration of AI models in sensitive applications like healthcare and finance, reducing the cost and risk associated with online testing.
This research provides a more accurate and nuanced method for evaluating how AI policies would perform without needing real-world deployment, accelerating development cycles and mitigating deployment risks.
- · AI developers
- · Recommendation systems
- · Healthcare AI
- · Reinforcement learning researchers
- · Traditional A/B testing methods
- · High-risk online experimentation
More reliable and ethical development of AI applications in high-stakes environments.
Faster deployment cycles for AI systems due to reduced need for costly online trials.
Enhanced trust in AI systems due to improved validation metrics leading to broader adoption across industries.
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Read at arXiv cs.LG