About Method Atlas
An interactive resource for learning causal inference methods — designed for graduate students in management, economics, and the social sciences.
Purpose
Method Atlas is an open-access, interactive resource for learning causal inference methods. It covers 27 methods, from the established workhorses (OLS, fixed effects, DiD) to frontier approaches (double/debiased ML, causal forests, synthetic DiD).
Each method includes interactive lessons, real-data examples from management research, and hands-on coding labs in R, Stata, and Python.
8 research practices complement the methods: sensitivity analysis, pre-registration, power analysis, randomization inference, specification curves, multiple testing corrections, clustering and few-cluster inference, and Lee bounds for attrition.
Together, they cover the full pipeline from research design to credible reporting.
Pedagogical Philosophy
Start with the problem
Each method is motivated by a concrete research question where naive analysis fails. You learn why the method exists before you learn how it works.
Intuition before math
Concepts are introduced through examples, simulations, and analogies. Formal notation comes later, after the intuition is in place. Mathematical details are always available in expandable derivations.
Active learning over passive reading
Interactive components, concept checks, mini-simulations, and coding labs appear throughout every lesson. Active engagement improves retention and transfer.
Honest about assumptions
Every method makes assumptions. This site makes those assumptions explicit, explains when they might fail, and shows you how to test and defend them.
Acknowledgments & Feedback
Acknowledgments
This project builds on the incredible work of the causal inference community. The methods presented here were developed by scholars across economics, statistics, management, political science, and epidemiology. The bibliography page provides comprehensive references for every method and practice.
Feedback
Found an error? Have a suggestion? This site is a living resource and contributions are welcome. Please reach out via email.