Results 281 to 290 of about 8,838,426 (323)

Causal Inference Meets Machine Learning

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Causal inference has numerous real-world applications in many domains such as health care, marketing, political science and online advertising. Treatment effect estimation, a fundamental problem in causal inference, has been extensively studied in statistics for decades. However, traditional treatment effect estimation methods may not well handle large-
Peng Cui 0001, Zheyan Shen, Sheng Li 0001, Liuyi Yao, Yaliang Li, Zhixuan Chu, Jing Gao 0004   +6 more
openaire   +2 more sources

Improving disaster resilience with causal machine learning for flood damage estimation.

Science of the Total Environment
Machine Learning (ML) models have become a pivotal tool in the scientific community, successfully addressing complex problems across various domains, including flood risk management.
Mujungu L. Museru, Rouzbeh Nazari, M. Nikoo, Maryam Karimi   +3 more
semanticscholar   +1 more source

Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption

International Conference on Machine Learning
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference.
Audrey Poinsot, Panayiotis N. Panayiotou, A. Leite, N. Chesneau, Ö. Şimşek, Marc Schoenauer   +5 more
semanticscholar   +1 more source

Using causal machine learning for predicting the risk of flight delays in air transportation

, 2021
Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is ...
Dothang Truong
semanticscholar   +1 more source

Optimizing Enterprise Decision-Making through Causal Machine Learning Models and Real-Time Business Intelligence Integration

International Journal of Research Publication and Reviews
As enterprises navigate increasingly complex, data-saturated environments, the limitations of traditional predictive analytics in strategic decision-making have become more pronounced.
Ishola Bayo Ridwan
semanticscholar   +1 more source

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Journal of Forecasting
The predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter ...
Jia-Ming Liu, Xuemei Zhang, Hai-Tao Xiong   +2 more
semanticscholar   +1 more source

Using GPT-4 to guide causal machine learning

Expert systems with applications
Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLMs ...
Anthony C. Constantinou, N. K. Kitson, Alessio Zanga   +2 more
semanticscholar   +1 more source

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Bioresource Technology
Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification.
Shi-Qi Liu, Zeqing Long, Jin-Song Liang, Jie Zhang, Duofei Hu, Pengfei Hou, Guangming Zhang   +6 more
semanticscholar   +1 more source

Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning

Foundations and Trends® in Machine Learning
Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finance. It is no longer far-fetched to envision a future where autonomous systems will drive entire business decisions and, more broadly, support large-scale decision-making ...
Drago Plecko, Elias Bareinboim
openaire   +3 more sources

Causal Machine Learning for Medical Texts

Text analysis has become increasingly common in medical research, especially for tasks like patient diagnosis based on medical notes. However, most existing approaches do not account for causal rela tionships between words and diagnoses. This paper proposes a causal approach using the MIMIC-III dataset to identify words or word pairs that causally ...
Alessandro Albano, Chiara Di Maria, Mariangela Sciandra, Antonella Plaia   +3 more
openaire   +1 more source