Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Jennings, Joel"'
Modeling true world data-generating processes lies at the heart of empirical science. Structural Causal Models (SCMs) and their associated Directed Acyclic Graphs (DAGs) provide an increasingly popular answer to such problems by defining the causal g
Externí odkaz:
http://arxiv.org/abs/2404.06969
Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance, and climate science. The dynamics of such systems are often best des
Externí odkaz:
http://arxiv.org/abs/2311.03309
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 persists in complex tasks such as causal inference, primarily due to challen
Externí odkaz:
http://arxiv.org/abs/2310.00809
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to joint inference over combinato
Externí odkaz:
http://arxiv.org/abs/2307.13917
Autor:
Zhang, Cheng, Bauer, Stefan, Bennett, Paul, Gao, Jiangfeng, Gong, Wenbo, Hilmkil, Agrin, Jennings, Joel, Ma, Chao, Minka, Tom, Pawlowski, Nick, Vaughan, James
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 current LLMs can answer causal questions with existing causal knowled
Externí odkaz:
http://arxiv.org/abs/2304.05524
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 (ADMGs), which describe ancestral relations between variables using direc
Externí odkaz:
http://arxiv.org/abs/2303.12703
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 contextual experiments using information-theoretic principles. After formulat
Externí odkaz:
http://arxiv.org/abs/2302.14015
Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthcare. Given the complexity of real-world relationships and the nature o
Externí odkaz:
http://arxiv.org/abs/2210.14706
Autor:
Gong, Wenbo, Smith, Digory, Wang, Zichao, Barton, Craig, Woodhead, Simon, Pawlowski, Nick, Jennings, Joel, Zhang, Cheng
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct
Externí odkaz:
http://arxiv.org/abs/2208.12610
The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving contextual treatment assignment decisions: these are personalised treatm
Externí odkaz:
http://arxiv.org/abs/2207.05250