The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data

In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. This helps formulate hypotheses regarding molecular mechanisms that could potentially be targeted by future medicines. The CausalBench Challenge was an initiative to invite the machine learning community to advance the state of the art in constructing gene-gene interaction networks. These networks, derived from large-scale, real-world datasets of single cells under various perturbations, are crucial for understanding the causal mechanisms underlying disease biology. Using the framework provided by the CausalBench benchmark, participants were tasked with enhancing the capacity of the state of the art methods to leverage large-scale genetic perturbation data. This report provides an analysis and summary of the methods submitted during the challenge to give a partial image of the state of the art at the time of the challenge. The winning solutions significantly improved performance compared to previous baselines, establishing a new state of the art for this critical task in biology and medicine.

@article{chevalley2025_2308.15395,
  title={ The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data },
  author={ Mathieu Chevalley and Jacob Sackett-Sanders and Yusuf Roohani and Pascal Notin and Artemy Bakulin and Dariusz Brzezinski and Kaiwen Deng and Yuanfang Guan and Justin Hong and Michael Ibrahim and Wojciech Kotlowski and Marcin Kowiel and Panagiotis Misiakos and Achille Nazaret and Markus Püschel and Chris Wendler and Arash Mehrjou and Patrick Schwab },
  journal={arXiv preprint arXiv:2308.15395},
  year={ 2025 }
}