Results 11 to 20 of about 1,964,226 (308)
Causal-learn: Causal Discovery in Python [PDF]
Journal of machine learning research, 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
Yujia Zheng +8 more
semanticscholar +3 more sources
An introduction to causal discovery
Swiss Journal of Economics and StatisticsIn social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims
Martin Huber
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Scalable Time Series Causal Discovery with Approximate Causal Ordering
MathematicsCausal discovery in time series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or samples.
Ziyang Jiao, Ce Guo, Wayne Luk
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Causal discovery for the microbiome. [PDF]
Lancet Microbe, 2022 Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch's classical postulates remind us
Corander J, Hanage WP, Pensar J.
europepmc +5 more sources
Semi-supervised Learning of Visual Causal Macrovariables
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Discovery of causally related concepts is one of the key challenges in extracting knowledge from observational data. Lower-dimensional “causal macrovariables” represent concepts which preserve all relevant causal information in high-dimensional systems ...
Aruna Jammalamadaka +6 more
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Whole-brain causal discovery using fMRI. [PDF]
Netw Neurosci, 2023 Abstract Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes.
Arab F +4 more
europepmc +4 more sources
Environmental Data Science, 2022 Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always ...
Xavier-Andoni Tibau +5 more
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Power Analysis for Causal Discovery. [PDF]
Int J Data Sci Anal, 2022 Abstract Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms.
Kummerfeld E, Williams L, Ma S.
europepmc +3 more sources
Greedy Causal Discovery Is Geometric
SIAM Journal on Discrete Mathematics, 2023 Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example the GES, GIES, and MMHC algorithms.
Svante Linusson +2 more
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Mining Causality via Information Bottleneck [PDF]
Jisuanji kexue, 2022 Causal discovery from observational data is a fundamental problem in many disciplines.However,existing methods such as constraint-based methods and causal function-based methods have strong assumptions on the causal mechanism of data,and are only ...
QIAO Jie, CAI Rui-chu, HAO Zhi-feng
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