Results 31 to 40 of about 477,715 (269)
Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts [PDF]
, 2009 Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal ...
Buehner, M, May, J
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, 2022 Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined.
Ni, Yang, Mallick, Bani
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PLoS ONE, 2023 Recently, the utilization of real-world medical data collected from clinical sites has been attracting attention. Especially as the number of variables in real-world medical data increases, causal discovery becomes more and more effective.
Hideaki Kawaguchi
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Causal-learn: Causal Discovery in Python
, 2023 Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery. This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers.
Zheng, Yujia +8 more
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Justifying additive-noise-model based causal discovery via algorithmic information theory [PDF]
, 2009 A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X for just two observed variables X and Y. It is based on the observation that there exist (non-Gaussian) joint distributions P(X,Y) for which Y may be ...
Bastian Steudel +7 more
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Causal Discovery with Continuous Additive Noise Models [PDF]
, 2014 We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available.
Janzing, Dominik +3 more
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A quantum causal discovery algorithm
, 2018 Finding a causal model for a set of classical variables is now a well-established task---but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems.
Costa, Fabio, Giarmatzi, Christina
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On Incorporating Prior Knowledge Extracted From Large Language Models Into Causal Discovery
IEEE AccessLarge Language Models (LLMs) can reason about causality by leveraging vast pre-trained knowledge and text descriptions of datasets, demonstrating their effectiveness even when data is scarce.
Chanhui Lee +12 more
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Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables [PDF]
, 2020 Given a response $Y$ and a vector $X = (X^1, \dots, X^d)$ of $d$ predictors, we investigate the problem of inferring direct causes of $Y$ among the vector $X$. Models for $Y$ that use all of its causal covariates as predictors enjoy the property of being
Christiansen, Rune, Peters, Jonas
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Causality discovery technology
The European Physical Journal Special Topics, 2012 Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling.
Chen, M +6 more
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