Results 11 to 20 of about 472,976 (271)
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.
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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
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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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Causal discovery using compression-complexity measures [PDF]
Journal of Biomedical Informatics, 2021 Accepted version with major revisions to results and discussion.
SY, Pranay, Nagaraj, Nithin
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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.
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Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning
Ocean-Land-Atmosphere Research, 2023 It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness, nonquantitativeness, computational inefficiency, etc.
Xin‐Zhong Liang +2 more
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Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach [PDF]
, 2020 Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions.
Carrara, A. +7 more
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Nonlinear causal discovery with confounders. [PDF]
J Am Stat Assoc, 2023 This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a
Li C, Shen X, Pan W.
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