ServiceNow Research

Gradient-Based Neural DAG Learning with Interventions

Abstract

Decision making based on statistical association alone can be a dangerous en- deavor due to non-causal associations. Ideally, one would rely on causal rela- tionships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter- ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures.

Publication
Workshop at the International Conference on Learning Representations (ICLR)
Philippe Brouillard
Philippe Brouillard
Visiting Researcher

Visiting Researcher at Human Decision Support located at Montreal, QC, Canada.

Alexandre Drouin
Alexandre Drouin
Research Lead

Research Lead at Human Decision Support located at Montreal, QC, Canada.

Alexandre Lacoste
Alexandre Lacoste
Research Scientist

Research Scientist at Human Decision Support located at Montreal, QC, Canada.