Mastering Causal Metrics

A hands-on AI-powered study guide to Mastering Causal Metrics. Learn the foundations of causal inference with interactive Python notebooks and AI tools.

The Furious Five: Five Methods to Separate Cause from Correlation — RCT, OLS, IV, RD, DiD

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Foundational Methods

Based on Mastering 'Metrics by Angrist & Pischke. Learn causal inference from randomized trials to differences-in-differences.

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Python Notebooks

Zero-installation Google Colab notebooks. Real datasets, working code, and complete implementations of every method.

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AI-Powered Learning

Multiple AI tutors with distinct pedagogical styles.

Curriculum

6 chapters organized around the five tools of causal inference

Part I — The Framework

01 Randomized Trials

Selection Bias · Potential Outcomes · RAND HIE

Part II — Quasi-Experiments

02 Regression

OLS · Omitted Variable Bias · Bad Controls

03 Instrumental Variables

LATE · Compliers · Minneapolis DV Experiment

04 Regression Discontinuity

Sharp RD · Bandwidth · MLDA and Mortality

05 Differences-in-Differences

Parallel Trends · Two-Way FE · Great Depression Banking

Part III — Synthesis

06 The Wages of Schooling

Twins · Quarter of Birth · Sheepskin Effects

Other Resources

Curated materials to deepen your causal inference toolkit

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