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Building the First Automated Causal Discovery Platform

Project description

The first automated causal discovery software engine

Causal discovery methods are not new. They have been used to identify potential cause-effect relationships from observational data for decades. Even though they are becoming increasingly important in science and industries, they are largely inaccessible to non-experts. The EU-funded AUTOCD project proposes to create the first automated causal discovery software engine and explore its commercial exploitation. AutoCD will increase the productivity of experts and allow the application of causal discovery with minimal expertise. Such automation relies on the outcomes the CAUSALPATH ERC project. AutoCD will compare the development of the growing industry of automated machine learning (AutoML) libraries and platforms.

Objective

Causal Discovery is desperately needed in both science and the industry, but it is largely inaccessible to non-experts. AutoCD proposes to create the first automated causal discovery software engine and explore its commercial exploitation. AutoCD will largely boost the productivity of experts as well as allow the application of causal discovery with minimal expertise. It will provide functionalities such as (a) induction of causal models and causal relations from data by automatically tuning the algorithmic causal discovery choices and their hyper-parameters, (b) inferences regarding the strength of causal effects and exploration of what-if scenarios of possible interventions. Such automation has only become recently possible due to research performed of the origin ERC named CAUSALPATH. We will work with two industrial partners, namely Gnosis Data Analysis and Huawei to validate AutoCD on real data and problems. Gnosis commercializes the JADBio product, which is a SaaS AutoML platform with obvious synergies to AutoCD. It has an expressed interest in AutoCD for a potential licensing deal (see letter of intent). AutoCD parallels the development of automated machine learning (AutoML) libraries and platforms that is growing to a $14bil industry. The project will create an MVP at TRL 5 and a business plan to commercialize the product. The research team (2 Profs, 1 Ph.D. student, 1 scientific programmer) have extensive collective experience not only inventing and designing novel causal discovery algorithms. In addition, the PI is also the co-founder of Gnosis with extensive experience in creating deep tech AutoML products and commercializing them. He will devote 70% of his research time to the project.

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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

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(opens in new window) ERC-2022-POC1

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Host institution

PANEPISTIMIO KRITIS
Net EU contribution

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.

€ 150 000,00
Address
UNIVERSITY CAMPUS GALLOS
74 100 RETHIMNO
Greece

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Region
Νησιά Αιγαίου Κρήτη Ρέθυμνο
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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