Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Abstract: In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge.
To overcome these challenges, this paper proposes a novel method for causal inference, in which SCD and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through “statistical causal prompting (SCP)” for LLMs and prior knowledge augmentation for SCD.
Experiments have revealed that the results of LLM-KBCI and SCD augmented with LLM-KBCI approach the ground truths, more than the SCD result without prior knowledge.
It has also been revealed that the SCD result can be further improved if the LLM undergoes SCP.
Furthermore, with an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM.
The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: The requested changes from reviewers have been reflected in red font.
We sincerely appreciate for all the reviewers ' fruitful comments. (July 17th, 2024)
Assigned Action Editor: ~Fabio_Stella1
Submission Number: 2730
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