Proceedings of the Annual Meeting of the Cognitive Science Society

Understanding the values, norms, behaviors, and causal beliefs of communities is a central goal of cognitive science, with practical benefits of grasping and improving community factors such as healthcare delivery. These cultural causal beliefs are evident, in part, within narratives, interview transcripts, ethnography, and other textual sources, but analyzing these texts presently involves tedious expert hand-coding or relatively shallow qualitative text analysis or classification. We present a novel approach for extracting graphical causal models from text via NLP, including qualitative causality, intentions, teleology, sentiment, welfare, social influence, and other rationale. The factors (i.e., nodes) of these causal models are tagged with ethnographic attributes and word-senses, allowing aggregation of causal models over thousands of passages to identify correlations and recurring themes. We apply this approach to a corpus of narrative interviews about maternal and child health and healthcare delivery in Bihar, India, corroborating the hand-coded results of human experts and also identifying novel insights about explanatory structure.