3rd Workshop on Causal Inference and Machine Learning in Practice

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference within machine learning models. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.

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Publication Details

Published
2025-08-03
DOI
https://doi.org/10.1145/3711896.3737858
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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3rd Workshop on Causal Inference and Machine Learning in Practice

Zeyu Zheng, Shaojiang Yin, Paul Lo, Jing Pan et al.
Anomaly Detection Techniques and Applications
article

3rd Workshop on Causal Inference and Machine Learning in Practice

Zeyu Zheng, Shaojiang Yin, Paul Lo, Jing Pan, Jingshen Wang, Jeong-Yoon Lee, Totte Harinen, Huigang Chen, Chu Wang, Yingfei Wang, Zhenyu Zhao, Yifeng Wu, Roland Stevenson
article en

Abstract

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference within machine learning models. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.

Bay Area Air Quality Management District (US), Google (United States) (US), Amazon (United States) (US), University of Washington (US), Uber AI (United States) (US), Creative Technologies (United States) (US), Pharmacology Research Institute (US), Snap (United States) (US), Seattle University (US), University of California, Berkeley (US)
Quality Education
Openalex Percentile: Top 90%
Anomaly Detection Techniques and Applications
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