mlsanityData to 7 Oct 2026

A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009

arXiv 2308.05451 ↗First posted 10 Aug 2023stat.ME cs.LG stat.APcited by 1

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Abstract (arXiv)

With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a forecast scenario. It further discusses some potential benefits and challenges for causal inference with time series forecasting when modeling the counterfactuals, estimating the uncertainty and incorporating prior knowledge to estimate causal effects in different forecasting scenarios.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 10 Aug 2023.

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4 of 7 references.

References · 4

  1. 2019Causality for Machine Learningcited by 242
  2. 2022Causal Machine Learning: A Survey and Open Problemscited by 107
  3. 2021Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspectivecited by 40
  4. 2022Bayesian Causal Inference: A Critical Reviewcited by 21

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