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UID:pretalx-sips2025-budapest-EWUKCG@pretalx.com
DTSTART;TZID=CET:20250626T145200
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DESCRIPTION:Causal claims are key to the evolution and consolidation of the
 oretical frameworks in social science. However\, manual identification of 
 causal claims\, extraction of cause-effect pairs\, and synthesis of them i
 nto direct acyclic graphs (DAGs) remains an important challenge. The volum
 e of literature\, cognitive biases in interpretation\, lack of transparenc
 y\, and reproducibility make manual synthesis ineffective and unreliable. 
 To overcome these challenges\, moving to a new paradigm using natural lang
 uage processing techniques (NLPs) is imperative.\nIn this talk\, I will pr
 esent our NLP pipeline that automates extracting causal claims from social
  science papers\, identifies cause-effect pairs with polarity (positive\, 
 negative\, neutral)\, and constructs DAGs representing these relationships
 . This pipeline uses large language models (LLMs) and other NLP techniques
  to improve the model precision in DAG construction. I will explain how th
 is system works and highlight its potential application to theoretical res
 earch and evidence synthesis.
DTSTAMP:20260514T172812Z
LOCATION:Underground\, p10
SUMMARY:LT36: Automating Causal Claim Extraction and DAG Construction from 
 Social Science Scholarly Papers Using an NLP Pipeline - Rasoul
URL:https://pretalx.com/sips2025-budapest/talk/EWUKCG/
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