SOURCE-LINKED INTELLIGENCE

Causal Inference for Exploration and Learning

CORDIS · observation · Publication date unknown

part from machines, this proposal advocates the integration of causal reasoning into sequential decision-making frameworks, such as bandits and reinforcement learning. Historically, the two fields of machine learning (and its subfield of sequential decision-making) and causality have developed separately. In addition to historical reasons, most machine learning algorithms are causally agnostic because causal inference is inherently a hard problem. The key objective of the proposed work is to enhance modern machine learning systems—especially in sequential decision-making settings such as multi-armed bandits, reinforcement learning (RL), and large language models (LLMs)—by systematically incorporating tools and principles from causal inference. The proposed enhancement will be achieved through the development of a novel framework for robust, flexible, and scalable causal inference that uses both interventional and counterfactual reasoning as opposed to traditional approaches that merely used observational data. The project will span theoretical endeavors as well as the design of pro

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recordType

award

status

SIGNED

region

EU

value

2499355

unit

EUR