MethodAtlas
5 Paths

Learning Paths

Curated paths through the material. Each path builds skills in a logical sequence. Your progress is saved locally in your browser.

Core Causal Inference

The essential path through foundational concepts and core design-based methods.

0% complete

  1. Why Causal Inference?

  2. The Anatomy of a Research Design

  3. Selection Bias and Confounding

  4. The Language of Identification

  5. DAGs for Beginners

  6. A Taxonomy of Identification Strategies

  7. Working with Data

  8. The Credibility Revolution

  9. Experimental Design

  10. OLS (Robust SEs, Clustering)

  11. Fixed Effects (Two-Way FE)

  12. Difference-in-Differences (Canonical 2×2)

  13. Event Studies (Dynamic Treatment Effects)

  14. Regression Discontinuity Design – Sharp

  15. Instrumental Variables / 2SLS

Cross-Sectional & Discrete Outcomes

Methods for cross-sectional data with various outcome types.

0% complete

  1. Why Causal Inference?

  2. Selection Bias and Confounding

  3. The Language of Identification

  4. Working with Data

  5. OLS (Robust SEs, Clustering)

  6. Logit / Probit

  7. Matching (PSM, CEM, NN, Weighting)

  8. Poisson / Negative Binomial

  9. Doubly Robust / AIPW Estimation

Advanced Design-Based Methods

Cutting-edge quasi-experimental methods. Requires the Core path.

0% complete

  1. Staggered DiD

  2. Synthetic Control

  3. Synthetic Difference-in-Differences

  4. Shift-Share / Bartik Instruments

  5. Regression Discontinuity Design – Fuzzy

Machine Learning Meets Causal Inference

ML methods for causal estimation and heterogeneous treatment effects. Requires the Core path first.

0% complete

  1. Matching (PSM, CEM, NN, Weighting)

  2. Double/Debiased Machine Learning (DML)

  3. Causal Forests / Heterogeneous Treatment Effects

  4. Sensitivity Analysis for Unobservables

  5. Multiple Hypothesis Testing

Mechanisms & Mediation

For management students: understanding how and why treatments work.

0% complete

  1. Why Causal Inference?

  2. Selection Bias and Confounding

  3. DAGs for Beginners

  4. OLS (Robust SEs, Clustering)

  5. Experimental Design

  6. Causal Mediation Analysis

  7. Sensitivity Analysis for Unobservables

  8. Specification Curve Analysis