The causal capabilities of large language models (LLMs)  is a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy.
We further our understanding of LLMs and their causal implications, considering the distinctions between different types of causal reasoning tasks, as well as the entangled threats of construct and measurement validity.
We find that LLM-based methods establish new state-of-the-art accuracy on multiple causal benchmarks. Algorithms based on GPT-3.5 and 4 outperform existing algorithms on a pairwise causal discovery task (97%, 13 points gain) , counterfactual reasoning task (92%, 20 points gain)  and actual causality (86% accuracy in determining necessary and sufficient causes in vignettes) .
At the same time, LLMs exhibit unpredictable failure modes and we provide some techniques to interpret their robustness. Crucially, LLMs perform these causal tasks while relying on sources of knowledge and methods distinct from and complementary to non-LLM based approaches. Specifically, LLMs bring capabilities so far understood to be restricted to humans, such as using collected knowledge to generate causal graphs or identifying background causal context from natural language. We envision LLMs to be used alongside existing causal methods, as a proxy for human domain knowledge and to reduce human effort in setting up a causal analysis, one of the biggest impediments to the widespread adoption of causal methods.
We also see existing causal methods as promising tools for LLMs to formalize, validate, and communicate their reasoning, especially in high-stakes scenarios. Our experiments donotimply that complex causal reasoning has spontaneously emerged in LLMs.
However, in capturing common sense and domain knowledge about causal mechanisms and supporting translation between natural language and formal methods, LLMs open new frontiers for advancing the research, practice, and adoption of causality.