Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns

Review Computer Science, Interdisciplinary Applications

Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices

Arash Teymori Gharah Tapeh et al.

Summary: This review aims to promote the integration of artificial intelligence techniques into the field of structural engineering. It provides a comprehensive analysis and review of commonly used algorithms, techniques, and best practices, with a focus on applications in earthquake, wind, fire engineering, etc.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2023)

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Article Operations Research & Management Science

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Soheyl Khalilpourazari et al.

Summary: This study introduces a novel hybrid reinforcement learning-based algorithm for predicting the COVID-19 pandemic and optimizing the allocation of healthcare system resources. The results show that the method has superiority in optimization problems, accurately reflects the trend of the epidemic, and provides important insights for decision makers.

ANNALS OF OPERATIONS RESEARCH (2022)

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Article Physics, Multidisciplinary

Causal network inference in a dam system and its implications on feature selection for machine learning forecasting

Prince Joseph Erneszer A. Javier et al.

Summary: This study applies causal inference methods to analyze the causal network of a dam system and demonstrates the consistency of the convergent cross mapping (CCM) method in simulating real dam systems. The results reveal the lagged effects between water levels, climate, weather, and water demands, and provide an approach for selecting input variables in predictive modeling.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2022)

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Article Computer Science, Information Systems

A Survey on Causal Inference

Liuyi Yao et al.

Summary: Causal inference is a critical research topic across various domains and has gained increasing attention in recent years. Estimating causal effects from observational data has become an appealing research direction, driven by the advancement of machine learning techniques. The diverse range of methods and wide applications make it significant for researchers and practitioners alike.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

Vasilis Syrgkanis et al.

Summary: In recent years, there has been a growing trend in academia and industry to utilize machine learning methods for measuring causal effects and designing optimal policies. Open source packages like CausalML and EconML offer a unified interface with various machine learning methods for causal inference. This tutorial will cover topics such as treatment effect estimators, model validations, and optimization algorithms, and demonstrate their application in industry use cases.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

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Review Engineering, Multidisciplinary