Building the First Automated Causal Discovery Platform

Introduction

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.

Objectives

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:

1. Induction of causal models and causal relations from data by automatically tuning the algorithmic causal discovery choices and their hyper-parameters.
2. Inferences regarding the strength of causal effects and exploration of what-if scenarios of possible interventions.

Background

Such automation has only become recently possible due to research performed by 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.

Industrial Partnerships

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).

Market Context

AutoCD parallels the development of automated machine learning (AutoML) libraries and platforms that is growing to a $14 billion industry. The project will create an MVP at TRL 5 and a business plan to commercialize the product.

Research Team

The research team consists of:

• 2 Professors
• 1 Ph.D. student
• 1 scientific programmer

They 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.