93 citations · 116 across the 6 of their papers we have counts for
6 papers
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira +5
Recently an influx of studies claim emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack syst…
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu +6
An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different…
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma +1
The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as m…
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
Sara Mohammad-Taheri, Jeremy Zucker, Charles Tapley Hoyt +4
Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requir…
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
Jeremy Zucker, Kaushal Paneri, Sara Mohammad-Taheri +8
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, c…
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
Robert Osazuwa Ness, Kaushal Paneri, Olga Vitek
This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfa…