We identify causal variables and their graphs from pairs of high-dimensional observations between which single interventions have been performed. Published at NeurIPS 2022.
We present ENCO, an efficient structure learning method that leverages observational and interventional data and scales to graphs with a thousand variables. Published at ICLR 2022.
We explore the application of normalizing flows on categorical data, and propose GraphCNF a permutation-invariant generative model on graphs. Published at ICLR 2021.