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Causal Reinforcement Learning
Elias Bareinboim

Mon Jul 13 05:00 AM -- 08:00 AM & Mon Jul 13 03:00 PM -- 06:00 PM (PDT) @ None

Causal inference provides a set of tools and principles that allows one to combine data and causal invariances about the environment to reason with questions of counterfactual nature -- i.e., what would have happened had reality been different, even when no data about this unrealized reality is available. Reinforcement Learning is concerned with efficiently finding a policy that optimizes a specific function (e.g., reward, regret) in interactive and uncertain environments. These two disciplines have evolved independently and with virtually no interaction between them. In fact, they operate over different aspects of the same building block, i.e., counterfactual relations, which makes them umbilically tied.

In this tutorial, we introduce a unified treatment putting these two disciplines under the same conceptual and theoretical umbrella. We show that a number of natural and pervasive classes of learning problems emerge when this connection is fully established, which cannot be seen individually from either discipline. In particular, we'll discuss generalized policy learning (a combination of online, off-policy, and do-calculus learning), where and where to intervene, counterfactual decision-making (and free-will, autonomy, Human-AI collaboration), police generalizability, causal imitation learning, among others. This new understanding leads to a broader view of what counterfactual learning is and suggests the great potential for the study of causality and reinforcement learning side by side, which we name causal reinforcement learning (CRL, for short).

Author Information

Elias Bareinboim (Columbia)
Elias Bareinboim

Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to artificial intelligence and machine learning as well as data-driven fields in the health and social sciences. His work was the first to propose a general solution to the problem of ``causal data-fusion,'' providing practical methods for combining datasets generated under different experimental conditions and plagued with various biases. In the last years, Bareinboim has been exploring the intersection of causal inference with decision-making (including reinforcement learning) and explainability (including fairness analysis). Before joining Columbia, he was an assistant professor at Purdue University and received his Ph.D. in Computer Science from the University of California, Los Angeles. Bareinboim was named one of ``AI's 10 to Watch'' by IEEE, and is a recipient of an NSF CAREER Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award.

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