Objective
The number of storks and newborn babies in a country are correlated, but neither causes the other. Uncovering which correlations signal an underlying causal relationship is a fundamental step in science, requiring the analysis of ever increasing amounts of raw, high-dimensional, temporal data. This creates an urgent need for computational aids by machine learning systems. Unfortunately, off-the-shelf models fail to draw causal conclusions, as they make predictions only using correlations. Even in well-designed experiments, the correlations they detect can be spurious, leading to mistakes and hallucinations that systematically affect conclusions when fed into causal reasoning pipelines.
My research project, Causal-AIM, tackles the challenge of turning pre-trained foundation models into scalable data-driven measurement devices for scientific experiments. My core hypothesis is that foundation models can be post-trained with suitable causal principles to become causally valid by design: capitalizing on the thriving ecosystem of open-source models, we build AI systems that yield causal rather than merely correlational interpretations.
To detect and quantify causality, thereby supporting scientists in their analysis workflow of experimental data, I propose methodologies for: (1) static, high-dimensional, raw data; (2) structured temporal data; and (3) generalization to new experiments without further training. These capabilities are integrated to support correct causal inference on temporal, multi-modal datasets arising from new scientific experiments. Alongside, I will release the first real-world benchmark for causal inference from high-dimensional data across scientific disciplines. Overall, Causal-AIM addresses key challenges in using AI models to support causal inferences, and its results will serve as a basis for groundbreaking interdisciplinary applications, including research on neurodevelopmental diseases, ecology, virtual cells, and extreme climate events.
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.
- medical and health sciences health sciences infectious diseases RNA viruses HIV
- natural sciences biological sciences ecology ecosystems
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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-2026-STG
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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.
3400 Klosterneuburg
Austria
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.