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Some argue scale is all what is needed to achieve AI, covering even causalmodels. We make it clear that large language models (LLMs) cannot be causal andgive reason onto why sometimes we might feel otherwise. To this end, we defineand exemplify a new subgroup of Structural Causal Model (SCM) that we call metaSCM which encode causal facts about other SCM within their variables. Weconjecture that in the cases where LLM succeed in doing causal inference,underlying was a respective meta SCM that exposed correlations between causalfacts in natural language on whose data the LLM was ultimately trained. If ourhypothesis holds true, then this would imply that LLMs are like parrots in thatthey simply recite the causal knowledge embedded in the data. Our empiricalanalysis provides favoring evidence that current LLMs are even weak `causalparrots.'

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal