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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Vydané v:	IEEE transactions on automation science and engineering Ročník 22; s. 10381 - 10391
Hlavní autori:	Yang, Xing, Lan, Tian, Qiu, Hao, Zhang, Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	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
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