  Causal inference is a vital aspect of multiple scientific disciplines and is
routinely applied to high-impact applications such as medicine. However,
evaluating the performance of causal inference methods in real-world
environments is challenging due to the need for observations under both
interventional and control conditions. Traditional evaluations conducted on
synthetic datasets do not reflect the performance in real-world systems. To
address this, we introduce CausalBench, a benchmark suite for evaluating
network inference methods on real-world interventional data from large-scale
single-cell perturbation experiments. CausalBench incorporates
biologically-motivated performance metrics, including new distribution-based
interventional metrics. A systematic evaluation of state-of-the-art causal
inference methods using our CausalBench suite highlights how poor scalability
of current methods limits performance. Moreover, methods that use
interventional information do not outperform those that only use observational
data, contrary to what is observed on synthetic benchmarks. Thus, CausalBench
opens new avenues in causal network inference research and provides a
principled and reliable way to track progress in leveraging real-world
interventional data.

Machine Learning