Diff Diff

Abstract

1. Introduction

A Python library for Difference-in-Differences (DiD) causal inference - sklearn-like estimators with statsmodels-style outputs, built for econometricians, marketing analysts, and data scientists running campaign-lift, policy, and staggered-rollout analyses.

2. Objective

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

This project demonstrates how Python can be applied to a real-world Digital Marketing & SEO problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/igerber/diff-diff.git
cd diff-diff

pip install diff-diff

git clone https://github.com/igerber/diff-diff.git
cd diff-diff
pip install -e ".[dev]"

import pandas as pd
from diff_diff import DifferenceInDifferences  # or: DiD

data = pd.DataFrame({
    'outcome': [10, 11, 15, 18, 9, 10, 12, 13],
    'treated': [1, 1, 1, 1, 0, 0, 0, 0],
    'post': [0, 0, 1, 1, 0, 0, 1, 1],
})

did = DifferenceInDifferences()
results = did.fit(data, outcome='outcome', treatment='treated', post='post')
print(results)              # DiDResults(ATT=3.0000, SE=1.7321, p=0.1583)
results.print_summary()     # full statsmodels-style table

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by igerber and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.