About Method Atlas

Method Atlas is an open-access, interactive resource for learning causal inference methods. It is designed for graduate students in management, economics, and the social sciences who need to understand and apply modern empirical methods in their research.

The site covers 20 causal inference 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.

7 research practices complement the methods: sensitivity analysis, pre-registration, power analysis, randomization inference, specification curves, multiple testing corrections, and partial identification bounds. Together, they cover the full pipeline from research design to credible reporting.

Start with the problem, not the solution. 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 for those who want them.

Active learning over passive reading. Interactive components, concept checks, mini-simulations, and coding labs appear throughout every lesson. A large body of evidence suggests that active engagement improves retention and transfer relative to passive instruction (Freeman et al., 2014, PNAS).

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. No method is presented as a magic bullet.