14 citations · 20 across the 4 of their papers we have counts for
4 papers
The Essential Role of Causality in Foundation World Models for Embodied AI
Tarun Gupta, Wenbo Gong, Chao Ma +11
Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents.…
Learned Causal Method Prediction
Shantanu Gupta, Cheng Zhang, Agrin Hilmkil
For a given causal question, it is important to efficiently decide which causal inference method to use for a given dataset. This is challenging because causal methods typically re…
Understanding Causality with Large Language Models: Feasibility and Opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett +8
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that…
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil +2
Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (AD…