Objective
Inspired by the fact that incorporation of causality in learning is what predominantly sets human judgment apart 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 provably good practical algorithms in the sense that they are simultaneously efficient in terms of computational requirements, as well as the number of data samples needed to perform causal identification and explorations in complex applications that arise in sequential decision-making in uncertain environments.
If successful, our findings will have significant broader impact on the society through a wide range of applications, such as clinical trials, assessing policy decisions that arise in daily life, in business, economics, and scientific domains, all the way to generative AI applications such as training large language models.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1015 Lausanne
Switzerland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.