Excited to share our #ICML paper introducing PACER, a scalable framework for causal discovery from large-scale interventional data.
PACER guarantees acyclicity by design, enabling direct optimization over valid causal structures and showing that scalable causal discovery is achievable through principled search-space design.
Across protein signaling networks and large-scale Perturb-seq benchmarks, PACER matches or outperforms state-of-the-art causal discovery methods while being much more efficient and scaling to thousands of variables.
Huge kudos to everyone involved in this work, especially Ramon Vinas Torne and three fantastic Master students Silvia Fabregas, Soyon Park and Ivo Ban 🚀
Paper: https://openreview.net/pdf?id=BS8upx3Smw
Code: https://github.com/mlbio-epfl/PACER
Project page: https://brbiclab.epfl.ch/pacer