Causal Discovery

Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems

We present iCITRIS, a causal representation learning method that can identify causal variables with instantaneous effects and their graph from temporal sequences. Published at ICLR 2023.

CITRIS - Causal Identifiability from Temporal Intervened Sequences

We present CITRIS, a causal representation learning algorithm for multidimensional causal factors identified from videos with interventions. Published at ICML 2022 (**Spotlight**).

ICAI Meetup: ENCO - Efficient Neural Causal Discovery

I gave an short introduction into causal discovery and our ICLR work "ENCO".

CausalUAI Contributed Talk: Efficient Neural Causal Discovery without Acyclicity Constraints

I gave a contributed talk on our paper "Efficient Neural Causal Discovery".

Efficient Neural Causal Discovery without Acyclicity Constraints

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.