CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generate data, yet existing studies differ substantially in graph families, mechanisms, and evaluation prot
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Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generate data, yet existing studies differ substantially in graph families, mechanisms, and evaluation prot
This paper introduces a unified and evolvable benchmark for causal discovery, which is crucial for evaluating the true causal reasoning abilities of foundation models beyond mere memorization. As AI systems are increasingly deployed in decision-making roles, understanding and validating their causal discovery capabilities becomes paramount for reliable and safe operation.