Results 11 to 20 of about 96,494 (250)
Assimilative causal inference [PDF]
Nature CommunicationsCausal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
Marios Andreou, Nan Chen, Erik Bollt
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Engineering, 2020 Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang +9 more
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Recursive Causal Inference Algorithm Based on Partial Correlation Test [PDF]
Jisuanji gongcheng, 2022 Causal inference is an important tool for mining relationships between observed data points.The causal inference algorithm encounters the problems of redundant tests and low test efficiency in high-dimensional cases, which limits the application of ...
CHEN Mingjie, ZHANG Hao, PENG Yuzhong, XIE Feng, PANG Yue
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International Journal of Public Health, 2023 Objectives: The aging of the South African population could have profound implications for the independence and overall quality of life of older adults as life expectancy increases. While there is evidence that lifetime socio-economic status shapes risks
Keletso Makofane +4 more
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Causal inference: relating language to event representations and events in the world
Frontiers in Psychology, 2023 Events are not isolated but rather linked to one another in various dimensions. In language processing, various sources of information—including real-world knowledge, (representations of) current linguistic input and non-linguistic visual context—help ...
Yipu Wei +3 more
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Introducing Causal Inference Using Bayesian Networks and do-Calculus
Journal of Statistics and Data Science Education, 2022 We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced ...
Yonggang Lu, Qiujie Zheng, Daniel Quinn
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On the dimensional indeterminacy of one-wave factor analysis under causal effects
Journal of Causal Inference, 2023 It is shown, with two sets of indicators that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one of the factors causally affects the other, then, in many settings, the ...
VanderWeele Tyler J. +1 more
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Simple yet sharp sensitivity analysis for unmeasured confounding
Journal of Causal Inference, 2022 We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
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Estimating marginal treatment effects under unobserved group heterogeneity
Journal of Causal Inference, 2022 This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the ...
Hoshino Tadao, Yanagi Takahide
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FAST RESTRICTED CAUSAL INFERENCE [PDF]
Demonstratio Mathematica, 2000 Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accelerating" the known CI algorithm of Spirtes, Glymour and Scheines {Spirtes:93}.
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