Portrait of Lazar Atanackovic is unavailable

Lazar Atanackovic

Alumni

Publications

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
Lazar Atanackovic
Alexander Tong
Jason Hartford
Leo Jingyu Lee
Bo Wang
Yoshua Bengio
Learning the causal structure of observable variables is a central focus for scientific discovery. Bayesian causal discovery methods tackle… (see more) this problem by learning a posterior over the set of admissible graphs given our priors and observations. Existing methods primarily consider observations from static systems and assume the underlying causal structure takes the form of a directed acyclic graph (DAG). In settings with dynamic feedback mechanisms that regulate the trajectories of individual variables, this acyclicity assumption fails unless we account for time. We focus on learning Bayesian posteriors over cyclic graphs and treat causal discovery as a problem of sparse identification of a dynamical sys-tem. This imposes a natural temporal causal order between variables and captures cyclic feedback loops through time. Under this lens, we propose a new framework for Bayesian causal discovery for dynamical systems and present a novel generative flow network architecture (DynGFN) tailored for this task. Our results indicate that DynGFN learns posteriors that better encapsulate the distributions over admissible cyclic causal structures compared to counterpart state-of-the-art approaches.
2023-01-01
arXiv.org (preprint)
doi.org
Bayesian Dynamic Causal Discovery
Alexander Tong
Lazar Atanackovic
Jason Hartford
Yoshua Bengio
Learning the causal structure of observable variables is a central focus for scientific discovery. Bayesian causal discovery methods tackle … (see more)this problem by learning a posterior over the set of admissible graphs that are equally likely given our priors and observations. Existing methods primarily consider observations from static systems and assume the underlying causal structure takes the form of a directed acyclic graph (DAG). In settings with dynamic feedback mechanisms that regulate the trajectories of individual variables, this acyclicity assumption fails unless we account for time. We treat causal discovery in the unrolled causal graph as a problem of sparse identification of a dynamical system. This imposes a natural temporal causal order between variables and captures cyclic feedback loops through time. Under this lens, we propose a new framework for Bayesian causal discovery for dynamical systems and present a novel generative flow network architecture (Dyn-GFN) tailored for this task. Dyn-GFN imposes an edge-wise sparse prior to sequentially build a k -sparse causal graph. Through evaluation on temporal data, our results show that the posterior learned with Dyn-GFN yields improved Bayes coverage of admissible causal structures relative to state of the art Bayesian causal discovery methods.
2022-11-30
NeurIPS.cc/2022/Workshop/CDS (poster)
openreview.net
openreview.net