# Review of The Book of Why: The New Science of Cause and Effect

arXiv: https://arxiv.org/abs/2003.11635 · HTML: https://mlsanity.com/paper/2003.11635/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering](https://mlsanity.com/paper/2207.12647/) (2022)
- [Large Language Models and the Reverse Turing Test](https://mlsanity.com/paper/2207.14382/) (2022)
- [Why did I fail? A Causal-based Method to Find Explanations for Robot Failures](https://mlsanity.com/paper/2204.04483/) (2022)
- [Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge](https://mlsanity.com/paper/2103.10245/) (2021)
- [Leaving Goals on the Pitch: Evaluating Decision Making in Soccer](https://mlsanity.com/paper/2104.03252/) (2021)
- [A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning](https://mlsanity.com/paper/2205.15016/) (2022)
- [A hybrid Bayesian network for medical device risk assessment and management](https://mlsanity.com/paper/2209.03352/) (2022)
- [Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry](https://mlsanity.com/paper/2309.11509/) (2023)
- [Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think](https://mlsanity.com/paper/2101.07855/) (2021)
- [Learning Robust Recommender from Noisy Implicit Feedback](https://mlsanity.com/paper/2112.01160/) (2021)
- [Causal factors discovering from Chinese construction accident cases](https://mlsanity.com/paper/2105.01227/) (2021)
- [Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence](https://mlsanity.com/paper/2111.13756/) (2021)
- [An Extension Of Combinatorial Contextuality For Cognitive Protocols](https://mlsanity.com/paper/2202.08209/) (2022)
- [Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation](https://mlsanity.com/paper/2209.05889/) (2022)
- [Causal DAG extraction from a library of books or videos/movies](https://mlsanity.com/paper/2211.00486/) (2022)
- [Lost in Algorithms](https://mlsanity.com/paper/2301.10333/) (2023)

## Abstract (arXiv)

Book review published as: Aronow, Peter M. and Fredrik S"avje (2020), "The Book of Why: The New Science of Cause and Effect." Journal of the American Statistical Association, 115: 482-485.