Results 11 to 20 of about 1,076,580 (347)
Simple yet sharp sensitivity analysis for unmeasured confounding
Journal of Causal Inference, 2022 We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
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Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition [PDF]
Knowledge Discovery and Data Mining, 2022 Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic losses. In the field of online service systems, operators rely on enormous monitoring data to detect and mitigate failures.
Mingjie Li +6 more
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Estimating marginal treatment effects under unobserved group heterogeneity
Journal of Causal Inference, 2022 This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the ...
Hoshino Tadao, Yanagi Takahide
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The MR-Base platform supports systematic causal inference across the human phenome
eLife, 2018 Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data.
G. Hemani +19 more
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Causal inference from cross-sectional earth system data with geographical convergent cross mapping
Nature Communications, 2023 Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which
Bingbo Gao +7 more
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Causalvis: Visualizations for Causal Inference [PDF]
International Conference on Human Factors in Computing Systems, 2023 Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaborations with domain experts.
G. Guo +3 more
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Nature Communications, 2023 Isolating the relationships between biodiversity and ecosystem functioning in natural ecosystems is challenging. Here, the authors apply a causal inference approach to observational data from grasslands and find a negative effect of biodiversity on ...
L. Dee +30 more
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Bayesian causal inference: a critical review [PDF]
Philosophical Transactions of the Royal Society A, 2022 This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects and ...
Fan-qun Li, Peng Ding, F. Mealli
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Methods and tools for causal discovery and causal inference
WIREs Data Mining Knowl. Discov., 2022 Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy.
Ana Rita Nogueira +4 more
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A Survey on Causal Inference [PDF]
ACM Transactions on Knowledge Discovery from Data, 2020 Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades.
Liuyi Yao +5 more
semanticscholar +1 more source