16 citations · 47 across the 6 of their papers we have counts for
6 papers
High Precision Causal Model Evaluation with Conditional Randomization
Chao Ma, Cheng Zhang
The gold standard for causal model evaluation involves comparing model predictions with true effects estimated from randomized controlled trials (RCT). However, RCTs are not always…
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil +3
Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks. However, a gap persis…
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…
Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis
Ruibo Tu, Chao Ma, Cheng Zhang
ChatGPT has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can. Thus,…
Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
Real-world datasets often have missing values associated with complex generative processes, where the cause of the missingness may not be fully observed. This is known as missing n…