Causal Inference for the Brave and True

Causal Inference for the Brave and True

Overview

Causal Inference for the Brave and True is an open-source, Python-based textbook by Matheus Facure that teaches causal inference methods through practical implementations. The book bridges the gap between theoretical econometrics textbooks and hands-on data science practice, presenting each method with runnable Python code, real-world datasets, and intuitive explanations that demystify the mathematics behind causal reasoning.

The handbook covers the full spectrum of causal inference techniques used in modern empirical research, from foundational concepts like potential outcomes and directed acyclic graphs (DAGs) through advanced methods including instrumental variables, regression discontinuity, difference-in-differences, and synthetic control. Each chapter builds on the previous one, constructing a coherent framework for thinking about causation from observational data.

With over 3,000 GitHub stars, this resource has become a standard reference for graduate students, applied researchers, and data scientists seeking to add causal reasoning to their analytical toolkit. The emphasis on Python implementation makes it directly applicable to modern research workflows.

Installation and Setup

The handbook runs as Jupyter notebooks. Set up the environment:

git clone https://github.com/matheusfacure/python-causality-handbook.git
cd python-causality-handbook

# Create a virtual environment
python -m venv causal-env
source causal-env/bin/activate

# Install dependencies
pip install numpy pandas matplotlib seaborn scikit-learn statsmodels
pip install linearmodels causalinference
pip install jupyter

Launch the notebook server:

jupyter notebook

The chapters are organized as numbered Jupyter notebooks, starting from foundational concepts and progressing to advanced methods. Each notebook is self-contained with all data loading and analysis code included.

Core Methods Covered

Potential Outcomes Framework: The book begins by establishing the Neyman-Rubin potential outcomes model, defining treatment effects and the fundamental problem of causal inference: