@article {LiF2024ForecasterReview,
	title = {Book Review of Causality: Models, Reasoning, and Inference, Judea Pearl. (Second Edition). (2009)},
	journal = {International Journal of Forecasting},
	volume = {40},
	number = {1},
	year = {2024},
	pages = {423{\textendash}425},
	abstract = {With the big popularity and success of Judea Pearl{\textquoteright}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.},
	keywords = {Computer Science - Machine Learning, Statistics - Applications, Statistics - Methodology},
	doi = {10.1016/j.ijforecast.2023.08.005},
	url = {http://arxiv.org/abs/2308.05451},
	author = {Feng Li}
}
