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

TL;DR

This review summarizes Judea Pearl's updated causality book, highlighting causal inference strategies in forecasting, and discusses benefits and challenges of causal analysis in time series forecasting, including counterfactuals and uncertainty estimation.

Contribution

It provides an accessible overview of the second edition of Pearl's causality book with a focus on forecasting applications and causal inference challenges.

Findings

Illustrates causal inference strategies in forecast scenarios

Discusses benefits of causal modeling in time series forecasting

Highlights challenges in counterfactuals and uncertainty estimation

Abstract

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.