Results 41 to 50 of about 1,964,226 (308)
Causal Discovery from Streaming Features [PDF]
2010 IEEE International Conference on Data Mining, 2010 In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available before learning begins. Feature generation and selection often have to be interleaved.
Kui Yu, Xindong Wu, Hao Wang, Wei Ding
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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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Methods and tools for causal discovery and causal inference
WIREs Data Mining Knowl. Discov., 2022 Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy.
Ana Rita Nogueira +4 more
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Survey and Evaluation of Causal Discovery Methods for Time Series
Journal of Artificial Intelligence Research, 2022 We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series.
Charles K. Assaad +2 more
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Exposing the Probabilistic Causal Structure of Discrimination [PDF]
, 2017 Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities.
Bonchi, Francesco +3 more
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RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model [PDF]
International Conference on Information and Knowledge ManagementIn the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks such as root cause analysis.
Peiwen Li +8 more
semanticscholar +1 more source
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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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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