16 citations · 58 across the 14 of their papers we have counts for
6 papers · 1 filter
Neural Structure Learning with Stochastic Differential Equations
Benjie Wang, Joel Jennings, Wenbo Gong
Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance…
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…
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings +3
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However…
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…
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
Desi R. Ivanova, Joel Jennings, Tom Rainforth +2
We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing context…