Reading & Evaluation
How to Read an Empirical Paper
A systematic framework for reading causal inference papers critically. Learn to identify the estimand, evaluate identification strategies, and judge credibility.
Guides
Practical guides for every stage of empirical research. From reading your first causal inference paper to writing up results, replicating studies, and avoiding common pitfalls.
Reading & EvaluationWriting & ReportingResearch WorkflowPitfalls & Anti-Patterns
Reading & Evaluation
Writing & Reporting
Research Workflow
Research Workflow
How to Replicate a Study
Step-by-step guide for replicating published causal inference papers. Covers finding data, reproducing results, diagnosing discrepancies, and extending analyses.
Research Workflow
Natural Experiment Workflow
Decision tree for identifying and exploiting natural experiments. From finding variation to choosing estimators, running diagnostics, and reporting results.
Research Workflow
Working with Observational Data
Workflow for causal inference from observational data. Covers estimator selection, managing the assumption burden, and building credibility without experiments.
Research Workflow
DiD vs. Synthetic Control: When to Use Which
A practical comparison of Difference-in-Differences and Synthetic Control methods, covering assumptions, strengths, and when each is appropriate. Includes the Synthetic DiD hybrid.
Research Workflow
Matching vs. IPW vs. Doubly Robust
A practical comparison of selection-on-observables estimators: matching, inverse probability weighting, and doubly robust/AIPW methods. Covers assumptions, tradeoffs, and when to use each.
Research Workflow
When to Use DML Over Standard OLS
A practical guide to choosing between OLS regression with controls and Double/Debiased Machine Learning. Covers high-dimensional confounders, flexible functional forms, and regularization bias.