Applied Causal Inference

Notes & simulations

Causal inference methods — each topic pairs explanation with an interactive simulation you can run in the browser.

Builds on: Statistical Inference

Framework

• Potential Outcomes & ATE — The framework behind all causal questions
• Identification vs Estimation — The distinction that organizes everything

Methods

• Selection on Observables — When conditioning on X is enough
• Difference-in-Differences — Parallel trends & treatment effects
• Instrumental Variables — Exogenous variation to isolate causal effects
• Regression Discontinuity — Cutoff rules as natural experiments
• Synthetic Control — Building a counterfactual from weighted donors

Panel Data

• Fixed vs Random Effects — Within vs between variation, the Hausman test, and when each estimator works

Estimation Tools

Tools that can be paired with different research designs.

• Regression Adjustment — Model the outcome, adjust for confounders
• Matching — Pair treated and control units on covariates
• Inverse Probability Weighting — Reweighting to balance treated and control
• Entropy Balancing — Exact moment balancing without a propensity score model
• Doubly Robust — Combine outcome model + propensity score for double protection