Learn causal inference with Python using the Brave and True handbook
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.
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.
Potential Outcomes Framework: The book begins by establishing the Neyman-Rubin potential outcomes model, defining treatment effects and the fundamental problem of causal inference:
import pandas as pd
import numpy as np
from scipy.stats import ttest_ind
# Estimate ATE from randomized experiment
treated = data[data["treatment"] == 1]["outcome"]
control = data[data["treatment"] == 0]["outcome"]
ate = treated.mean() - control.mean()
t_stat, p_value = ttest_ind(treated, control)
print(f"ATE: {ate:.3f}, p-value: {p_value:.4f}")
Regression and Matching: OLS regression for causal estimation, understanding omitted variable bias, propensity score methods, and matching estimators:
import statsmodels.formula.api as smf
# OLS with controls
model = smf.ols("outcome ~ treatment + age + income + education", data=data)
results = model.fit(cov_type="HC1")
print(results.summary().tables[1])
Instrumental Variables: Two-stage least squares and the local average treatment effect, with practical guidance on instrument validity and weak instrument diagnostics:
from linearmodels.iv import IV2SLS
# Two-stage least squares
iv_formula = "outcome ~ 1 + [treatment ~ instrument]"
iv_model = IV2SLS.from_formula(iv_formula, data=data)
iv_results = iv_model.fit(cov_type="robust")
print(iv_results.summary)
Difference-in-Differences: Parallel trends assumption, two-way fixed effects, event study designs, and staggered treatment adoption:
# Difference-in-Differences with two-way fixed effects
did_model = smf.ols(
"outcome ~ treated_post + C(unit_id) + C(time_period)",
data=panel_data
)
did_results = did_model.fit(cov_type="cluster", cov_kwds={"groups": panel_data["unit_id"]})
Regression Discontinuity: Sharp and fuzzy RD designs, bandwidth selection, and local polynomial estimation for identifying causal effects at policy thresholds.
Synthetic Control: Constructing counterfactual units from donor pools for comparative case studies, with inference via placebo tests.
Graduate Coursework: The handbook maps directly to applied econometrics and causal inference course syllabi. Students can follow along with lectures by running the corresponding notebooks, experimenting with parameter changes, and observing how different assumptions affect estimates.
Method Selection Guide: Use the decision framework presented across chapters to choose the appropriate method for your research question:
Replication and Extension: Each chapter uses real or realistic datasets. Researchers can adapt the code to their own data by replacing data loading steps while preserving the analytical pipeline.
Teaching Tool: Instructors can assign chapters as interactive homework, asking students to modify assumptions, change specifications, or apply methods to new datasets. The notebook format makes it straightforward to create assignments with embedded solutions.