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Typing assumptions improve identification in causal discovery
Philippe Brouillard · Perouz Taslakian · Alexandre Lacoste · Sébastien Lachapelle · Alexandre Drouin

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in Causal Inference & Machine Learning: Why now? »

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.

Author Information

Philippe Brouillard (Mila)
Perouz Taslakian (Samsung Electronics)
Alexandre Lacoste (ServiceNow)
Sébastien Lachapelle (Mila)
Alexandre Drouin (ServiceNow)

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