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

arXiv: https://arxiv.org/abs/2308.05451 · HTML: https://mlsanity.com/paper/2308.05451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective](https://mlsanity.com/paper/2111.14820/) (2021)
- [Bayesian Causal Inference: A Critical Review](https://mlsanity.com/paper/2206.15460/) (2022)

## Built on by (most cited first, 1 of 1)

- [Causal Synthetic Data Generation in Recruitment](https://mlsanity.com/paper/2511.16204/) (2025)

## 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.