Words as Difference Makers: How Large Language Models Determine Causal Structure in Text

• URL: http://arxiv.org/abs/2606.22430v1
• Date: Sun, 21 Jun 2026 10:40:47 GMT
• Title: Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
• Abstract summary: I argue that large language models (LLMs) employ a specific inductive approach based on a difference-making logic.<n>I analyze specific architectural features of LLMs to determine their roles in variational induction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' representing causal and definitional structure. However, the dominant formalisms of modern causal inference -- Judea Pearl's interventionist approach and the Neyman-Rubin potential outcomes framework -- struggle to illuminate how LLMs learn causal structure. I resolve this puzzle by arguing that LLMs employ a specific inductive approach based on a difference-making logic -- sometimes called variational induction. I demonstrate how central aspects of this logic are realized during training, where LLMs require enormous amounts of text data from a wide range of contexts to identify difference- and indifference-makers within word sequences. Furthermore, I analyze specific architectural features of LLMs -- such as token embeddings and self-attention -- to determine their roles in variational induction. The difference-making logic of LLMs fundamentally parallels the experimental method, where causal relations are derived by systematically varying individual circumstances to determine their influence on a phenomenon.

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