Clear, accessible explanations of new research, methods, and tools in causal inference and experimentation. We break down complex ideas without losing rigor.
A hybrid of synthetic control and DiD that inherits the strengths of both. We unpack the SDiD estimator and what makes it tick.
How do you turn an arbitrary threshold into a credible experiment? A deep dive into RDD—from the basic intuition to optimal bandwidth selection.
When your controls are high-dimensional, standard OLS breaks down. Double Machine Learning uses cross-fitting and Neyman orthogonality to recover valid causal estimates while letting machine learning handle the nuisance functions.