Mastering Causal Metrics

Welcome to Mastering Causal Metrics!

An AI-powered study guide to Mastering Causal Metrics. Learn the foundations of causal inference with interactive Python notebooks and AI tools, based on the foundational textbook Mastering ‘Metrics: The Path from Cause to Effect by Angrist & Pischke.

This platform features:

• Foundational Methods — Based on Mastering ‘Metrics by Angrist & Pischke. Learn causal inference from randomized trials to differences-in-differences.
• Python Notebooks — Zero-installation Google Colab notebooks. Real datasets, working code, and complete implementations of every method.
• AI-Powered Learning — Multiple AI tutors with distinct pedagogical styles.

Interactive Google Colab Notebooks

Click any badge below to open and run immediately in your browser:

Part I: The Framework

Chapter	Title	Topics	Colab Notebook
1	Randomized Trials	Selection Bias, Potential Outcomes, RAND HIE

Part II: The Five Tools

Chapter	Title	Topics	Colab Notebook
2	Regression	OLS, Omitted Variable Bias, Bad Controls
3	Instrumental Variables	LATE, Compliers, Minneapolis DV Experiment
4	Regression Discontinuity	Sharp RD, Bandwidth, MLDA and Mortality
5	Differences-in-Differences	Parallel Trends, Two-Way FE, Great Depression Banking

Part III: Synthesis

Chapter	Title	Topics	Colab Notebook
6	The Wages of Schooling	Twins, Quarter of Birth, Sheepskin Effects

How to Use the Notebooks

1. Click any “Open in Colab” badge above
2. Sign in with your Google account (free)
3. Click “Run All” in the Runtime menu (or run cells individually)
4. Explore and modify — change parameters, try different models, experiment with the data
5. Save your work — File > Save a copy in Drive to keep your modifications

No installation, no downloads, no setup required!

Authors and Credits

Carlos Mendez — Python implementation and educational notebook development

Joshua D. Angrist & Jörn-Steffen Pischke — Original textbook, Mastering ‘Metrics