CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

Understand

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.