The Effective Statistician - in association with PSI Behind the Scenes of Writing a Book About Casual Inference
Feb 17, 2025
In this engaging discussion, statistician Justin Belair shares his journey in writing about causal inference. He highlights the challenges of balancing technical writing with consulting work, emphasizing the need for a distraction-free environment. Justin reveals the importance of practical applications alongside theory and inspires with insights on effective writing strategies. He also explores the role of AI tools in enhancing writing while preserving an author's voice. Ideal for statisticians and aspiring authors, this conversation is packed with valuable takeaways!
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Episode notes
Initial Skepticism Turned Interest
- Justin Belair initially dismissed Judea Pearl's causal inference work as simplistic.
- He later found it intriguing after reading Pearl's book and taking courses.
Applying Causal Inference
- Apply causal inference in real-world data analysis, like addressing confounding in observational studies.
- Use techniques like adjustment or propensity score methods to mitigate causal problems.
Bridging the Gap
- Causal inference books often lack practical exercises and code examples.
- Belair's book offers hands-on learning with code and real-world data for better understanding.