Dhanya Sridhar

Assistant Professor

Curriculum vitae

dhanya.sridhar <at> mila.quebec


University of Montreal, Mila

F.04, 6666 Rue St. Urbain

Scalable Probabilistic Causal Structure Discovery

Journal article

Dhanya Sridhar, J. Pujara, L. Getoor
IJCAI, 2018

Semantic Scholar DBLP DOI


APA   Click to copy
Sridhar, D., Pujara, J., & Getoor, L. (2018). Scalable Probabilistic Causal Structure Discovery. IJCAI.

Chicago/Turabian   Click to copy
Sridhar, Dhanya, J. Pujara, and L. Getoor. “Scalable Probabilistic Causal Structure Discovery.” IJCAI (2018).

MLA   Click to copy
Sridhar, Dhanya, et al. “Scalable Probabilistic Causal Structure Discovery.” IJCAI, 2018.

BibTeX   Click to copy

  title = {Scalable Probabilistic Causal Structure Discovery},
  year = {2018},
  journal = {IJCAI},
  author = {Sridhar, Dhanya and Pujara, J. and Getoor, L.}


Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery.  We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings.


Follow this website

You need to create an Owlstown account to follow this website.

Sign up

Already an Owlstown member?

Log in