Results 21 to 30 of about 134,347 (253)
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
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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
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Power Analysis for Causal Discovery. [PDF]
Int J Data Sci Anal, 2022 Abstract Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms.
Kummerfeld E, Williams L, Ma S.
europepmc +3 more sources
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
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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
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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
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Local Causal Discovery for Estimating Causal Effects
CoRR, 2023 Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Shantanu Gupta +2 more
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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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Differentiable Causal Backdoor Discovery
CoRR, 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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PLoS ONE, 2023 Recently, the utilization of real-world medical data collected from clinical sites has been attracting attention. Especially as the number of variables in real-world medical data increases, causal discovery becomes more and more effective.
Hideaki Kawaguchi
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