Notes

Statistics, causal inference, and Bayesian thinking through interactive simulations.

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Foundations of Statistical Inference

Simulation-based modules for statistics, econometrics, and causal inference.

Distributions Sampling CLT LLN p-values & CIs Power & MDE Monte Carlo Regression & CEF Clustered SEs Bias-Variance Bayesian Updating Simpson's Paradox

Applied Causal Inference

From potential outcomes to modern treatment effect estimators — see selection bias, parallel trends, and reweighting in action.

Potential Outcomes Identification vs Estimation Selection on Observables Difference-in-Differences IV RDD Synthetic Control Regression Adjustment IPW Matching Entropy Balancing Doubly Robust Fixed vs Random Effects

Introduction to Bayesian Thinking

Priors, posteriors, shrinkage, and the Bayesian-frequentist divide — built from first principles with no measure theory.

Bayes' Theorem Priors & Posteriors Shrinkage Bayesian vs Frequentist MCMC Hierarchical Models