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In drug discovery, mapping interactions between genes within cellular systemsis a crucial early step. This helps formulate hypotheses regarding molecularmechanisms that could potentially be targeted by future medicines. TheCausalBench Challenge was an initiative to invite the machine learningcommunity to advance the state of the art in constructing gene-gene interactionnetworks. These networks, derived from large-scale, real-world datasets ofsingle cells under various perturbations, are crucial for understanding thecausal mechanisms underlying disease biology. Using the framework provided bythe CausalBench benchmark, participants were tasked with enhancing the capacityof the state of the art methods to leverage large-scale genetic perturbationdata. This report provides an analysis and summary of the methods submittedduring the challenge to give a partial image of the state of the art at thetime of the challenge. The winning solutions significantly improved performancecompared to previous baselines, establishing a new state of the art for thiscritical task in biology and medicine.

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