Typing Assumptions Improve Identification in Causal Discovery


Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. Under assumptions about the data-generative process, the causal graph can often be identified up to an equivalence class. Proposing new realistic assumptions to circumscribe such equivalence classes is an active field of research. In this work, we propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables. We thus introduce typed directed acyclic graphs, in which variable types are used to determine the validity of causal relationships. We demonstrate, both theoretically and empirically, that the proposed assumptions can result in significant gains in the identification of the causal graph.

Apr 11, 2022 4:00 AM — 4:00 AM
Oral presentation at CLeaR 2022 (Eureka, CA)
Philippe Brouillard
Philippe Brouillard
PhD Student

My research interests include causal discovery, causal representation learning, machine learning.