Learn how to find causal answers to your everyday questions using R and Python.
At everydaycausal.com you will learn how to estimate, test, and explain causal impacts. You don't need any specific background to master the ideas there. Every concept is broken down step-by-step, with relatable examples from e-commerce, fintech, and digital businesses.
You can read the book without running any code, but following along with the exercises is the best way to learn. To do that, you'll need R or Python installed.
All datasets used in the book are available in the data folder. You can either:
- Download manually from the folder above, or
- Load directly in your code by passing the raw URL to
read.csv()in R orpd.read_csv()in Python:
https://raw.githubusercontent.com/RobsonTigre/everyday-ci/main/data/<filename>.csv
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If you use this material in your work, please cite:
Tigre, Robson. Everyday Causal Inference: How to estimate, test, and explain impacts with R and Python. https://www.everydaycausal.com/
Copyright © 2025 by Robson Tigre. All rights reserved.
You may: Read the book and run the code for personal learning; Share official links to the book or repository; Cite short excerpts with proper attribution.
You may not: Reproduce or redistribute the text, code, or data; Use any part of this work to train AI or machine learning systems; Create competing courses, tutorials, or educational products based on this material; Build commercial training datasets from this content; Develop or market products using this book's name or its variants.
This content is for educational purposes only and does not constitute professional advice. All code is provided "as is," without warranty. The author disclaims all liability for outcomes based on this material. See the full legal notice for details.