Results 11 to 20 of about 1,819,138 (329)
CausalFormer: An Interpretable Transformer for Temporal Causal Discovery [PDF]
IEEE Transactions on Knowledge and Data EngineeringTemporal causal discovery is a crucial task aimed at uncovering the causal relations within time series data. The latest temporal causal discovery methods usually train deep learning models on prediction tasks to uncover the causality between time series.
Lingbai Kong, Wengen Li, Hanchen Yang
exaly +2 more sources
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.
europepmc +4 more sources
CoRR, 2022 It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities.
Ruta Binkyte-Sadauskiene +4 more
openaire +5 more sources
Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models [PDF]
BiomedicinesBackground/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes.
Mi Jin Noh, Yang Sok Kim
doaj +2 more sources
A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyA representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task.
Francesco Canonaco +5 more
doaj +2 more sources
Post-selection inference for causal effects after causal discovery. [PDF]
BiometrikaAlgorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation
Chang TH, Guo Z, Malinsky D.
europepmc +2 more sources
Can algorithms replace expert knowledge for causal inference? A case study on novice use of causal discovery. [PDF]
Am J EpidemiolWith growing interest in causal inference and machine learning among epidemiologists, there is increasing discussion of causal discovery algorithms for guiding covariate selection.
Gururaghavendran R, Murray EJ.
europepmc +2 more sources
J. Mach. Learn. Res.Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains.
Ehsan Mokhtarian +3 more
semanticscholar +5 more sources
Introduction to the foundations of causal discovery [PDF]
International Journal of Data Science and Analytics, 2016 This article presents an overview of several known approaches to causal discovery. It is organized by relating the different fundamental assumptions that the methods depend on. The goal is to indicate that for a large variety of different settings the assumptions necessary and sufficient for causal discovery are now well understood.
Frederick Eberhardt +1 more
exaly +2 more sources
AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways [PDF]
Frontiers in GeneticsIntroductionCardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases,
Adèle H. Ribeiro +7 more
doaj +2 more sources