Results 21 to 30 of about 1,819,138 (329)
Scalable Causal Discovery with Score Matching [PDF]
CLEaR, 2023 This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function $
Francesco Montagna +4 more
semanticscholar +2 more sources
Correction: A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyFrancesco Canonaco +5 more
doaj +2 more sources
Collective Causal Relations Discovery Algorithm for Multivariate Time-Series [PDF]
Jisuanji gongcheng, 2023 Causal discovery from multivariate time-series is a significant and fundamental problem in numerous disciplines.The existing multivariate time-series causal discovery methods learn the causal relations for each individual while some individuals may share
CAI Ruichu, WU Yunjin, CHEN Wei, HAO Zhifeng
doaj +1 more source
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
doaj +1 more source
Score matching enables causal discovery of nonlinear additive noise models [PDF]
International Conference on Machine Learning, 2022 This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal ...
Paul Rolland +6 more
semanticscholar +1 more source
Causal KL: Evaluating Causal Discovery
CoRR, 2021 26 ...
Rodney T. O'Donnell +2 more
openaire +2 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
openaire +2 more sources
, 2022 Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined.
Yang Ni, Bani K. Mallick
openaire +3 more sources
Optimal transport for causal discovery
CoRR, 2022 To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use.
Tu, Ruibo +3 more
openaire +4 more sources
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
doaj +1 more source