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Causal KL: Evaluating Causal Discovery
, 2021 26 ...
O'Donnell, Rodney T. +2 more
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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
Differentiable Causal Backdoor Discovery
, 2020 Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is
Gultchin, L +3 more
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Causal Discovery of Flight Service Process Based on Event Sequence
Journal of Advanced Transportation, 2021 The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains ...
Qian Luo +4 more
doaj +1 more source
Discovering Ecological Relationships in Flowing Freshwater Ecosystems
Frontiers in Ecology and Evolution, 2022 Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not ...
Konrad P. Mielke +9 more
doaj +1 more source
Learning latent functions for causal discovery
Machine Learning: Science and Technology, 2023 Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions.
Emiliano Díaz +3 more
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Markov Boundary Discovery with Ridge Regularized Linear Models
Journal of Causal Inference, 2016 Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association ...
Strobl Eric V., Visweswaran Shyam
doaj +1 more source
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach [PDF]
, 2020 Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions.
Carrara, A. +7 more
core +2 more sources
Causal Discovery from Streaming Features [PDF]
2010 IEEE International Conference on Data Mining, 2010 In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available before learning begins. Feature generation and selection often have to be interleaved.
Kui Yu, Xindong Wu, Hao Wang, Wei Ding
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Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models
Entropy, 2022 This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders.
Feng Xie +5 more
doaj +1 more source