Results 291 to 300 of about 1,076,580 (347)

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

North American Chapter of the Association for Computational Linguistics
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian J. McAuley, Wei Ai, Furong Huang   +12 more
semanticscholar   +1 more source

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
semanticscholar   +1 more source

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
semanticscholar   +1 more source

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
semanticscholar   +1 more source

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.
openaire   +1 more source

Causal inference for time series analysis: problems, methods and evaluation

Knowledge and Information Systems, 2021
Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting and clustering have been proposed to analyze ...
Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, A. Raglin, Huan Liu   +7 more
semanticscholar   +1 more source

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
Issa J. Dahabreh, Kirsten Bibbins-Domingo   +1 more
semanticscholar   +1 more source

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
semanticscholar   +1 more source

Causal Inference

Journal of Statistical Computation and Simulation, 2022
Fabian Dablander, Riet van Bork
  +5 more sources

On Pearl’s Hierarchy and the Foundations of Causal Inference

Probabilistic and Causal Inference, 2022
E. Bareinboim, Juan D Correa, D. Ibeling, Thomas F. Icard   +3 more
semanticscholar   +1 more source