Learning priors for data-efficient causal discovery

The inference of causal relationships is a problem of fundamental interest in science. Compared to models that rely on mere correlations, causal models allow us to anticipate the effect of a change in a system. Such causal models have applications ranging from government policy making to personalized medicine. However, learning causal models from data is a challenging task, since it requires large data sets and, in some cases, the conduction of costly or invasive experiments. In this project, we propose a new method to learn causal models using less data. Our approach relies on the inclusion of prior knowledge derived from known causal relationships and meta-information related to the problem of interest. Our work is expected to increase the applicability of such methods in settings where data is scarce (e.g., biological and medical data).

Philippe Brouillard
Superviseur universitaire: 
Alain Tapp
Partner University: