Nonlinear Causal Discovery via Dynamic Latent Variables

Distinguishing causality from mere correlation is a cornerstone in empirical research, as conflating the two can result in significant errors in decision-making, affecting policy formulation and the validity of scientific inferences. Traditional experimental designs, such as randomized trials, often...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 22; S. 10381 - 10391
Hauptverfasser:	Yang, Xing, Lan, Tian, Qiu, Hao, Zhang, Chen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2025
Schlagworte:	Causal discovery Cause effect analysis Economics Estimation expectation maximization Gaussian process Gaussian processes Heuristic algorithms Inference algorithms Kernel Markov chain Monte Carlo Mathematical models Monte Carlo methods Numerical analysis state space model
ISSN:	1545-5955, 1558-3783
Online-Zugang:	Volltext
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