v6

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, short

Summary

This volume, "Causality: Objectives and Assessment," compiles papers from a workshop held at NIPS 2008 on December 12, 2008, in Whistler, Canada, edited by Isabelle Guyon, Dominik Janzing, and Bernhard Schölkopf. It features foundational contributions to causal inference, including Judea Pearl's work, discussions on Directed Acyclic Graphs (DAGs), and various algorithms for causal discovery, such as sparse methods and Bayesian approaches. The collection also addresses practical challenges in distinguishing cause from effect, exploring techniques like nonlinear acyclic causal models and structure learning in cyclic networks, alongside methods for causal learning without relying on DAGs. Further contributions delve into causal modeling with multivariate time series data, the use of Boolean networks, and benchmarking causality measures like "Granger Causality" for effective monitoring and process control.

Key takeaway

This NIPS 2008 workshop proceedings provides a comprehensive overview of causal inference and discovery, addressing foundational principles and practical challenges. It details diverse methodologies, including graphical causal models, Bayesian algorithms, sparse causal discovery in time series, and techniques for distinguishing cause from effect using nonlinear models, alongside critical perspectives on DAGs. This resource is vital for AI/ML professionals seeking to build robust predictive systems, understand complex system dynamics, and enable targeted interventions by moving beyond correlational analysis.

Topics

• Causal Inference
• Causal Discovery
• Directed Acyclic Graphs
• Causal Structure Learning
• Time Series Causality

Best for: AI Researcher, AI Scientist, Data Scientist

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• v218 — This volume presents the proceedings of "The KDD'23 Workshop on Causal Discovery, Prediction and Decision," held on August 7, 2023, in Long Beach, USA, featuring contributions from numerous editors and authors.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.