Results 201 to 210 of about 1,239,696 (252)

Foundations and Future Directions for Causal Inference in Ecological Research.

Ecology Letters
Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges ...
K. Siegel, L. Dee
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Predictive models aren't for causal inference.

Ecology Letters, 2022
Ecologists often rely on observational data to understand causal relationships. Although observational causal inference methodologies exist, predictive techniques such as model selection based on information criterion (e.g. AIC) remains a common approach
Suchinta Arif, A. Macneil
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Causal Estimation and Causal Inference

2020
This entry provides an introduction to causal inference. Causal inference refers to the estimation of the effect of a treatment, policy, or intervention on an outcome of interest. Causal inference is, therefore, at the centre of science and social sciences. This entry emphasises Rubin’s potential outcomes framework.
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Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals.

Journal of the American Medical Association (JAMA)
Importance Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods
I. Dahabreh, Kirsten Bibbins-Domingo
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Show, Deconfound and Tell: Image Captioning with Causal Inference

Computer Vision and Pattern Recognition, 2022
The transformer-based encoder-decoder framework has shown remarkable performance in image captioning. However, most transformer-based captioning methods ever overlook two kinds of elusive confounders: the visual confounder and the linguistic confounder ...
Bing Liu, Dong Wang, Xu Yang, Yong Zhou, Rui Yao, Zhiwen Shao, Jiaqi Zhao   +6 more
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Causal inference in perception

Trends in Cognitive Sciences, 2010
Until recently, the question of how the brain performs causal inference has been studied primarily in the context of cognitive reasoning. However, this problem is at least equally crucial in perceptual processing. At any given moment, the perceptual system receives multiple sensory signals within and across modalities and, for example, has to determine
Ladan, Shams, Ulrik R, Beierholm
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Causal graph inference

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
We provide a framework to infer causal relationships in a system of multivariate, stochastic, delayed signals, with application to their prediction. First we address the dimensionality problem in information causality estimation and propose a method to improve the efficiency of calculations by retaining only the most essential components.
Simona Poilinca, Jhanak Parajuli, Giuseppe Abreu   +2 more
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Causal inference for clinicians

BMJ Evidence-Based Medicine, 2019
Evidence-based medicine (EBM) calls on clinicians to incorporate the ‘best available evidence’ into clinical decision-making. For decisions regarding treatment, the best evidence is that which determines the causal effect of treatments on the clinical outcomes of interest.
Steven D Stovitz, Ian Shrier
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Causal Inference by Compression

2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Causal inference is one of the fundamental problems in science. In recent years, several methods have been proposed for discovering causal structure from observational data. These methods, however, focus specifically on numeric data, and are not applicable on nominal or binary data. In this work, we focus on causal inference for binary data. Simply put,
Budhathoki, K., Vreeken, J.
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Applied Causal Inference Powered by ML and AI

arXiv.org
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs),
V. Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis   +4 more
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