Causal Discovery from Multiple Environments

At AAAI 2023 my colleagues Osman Mian, Jilles Vreeken and me presented our paper “Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments” in which we learn a global structural causal model over multiple environments, as well as discover potential local intervention that change some causal relationships within particular environments.

For medical data this has an enormous impact: Being able to reliably detect causal relationships in medical data, such as gene expressions or patient records, allows us not only to build more reliable and trustworthy models, but also to detect novel insights on diseases and risk factors.

Reliably detecting causal relationships requires large amounts of observational data, though. Therefore, it is paramount to develop privacy-preserving methods to tap into the large, but inherently distributed medical datasets in hospitals all over the world. What we need is federated causal discovery.