1. [[Design Pattern I - Instrumental Variable (IV)]] $\quad$ 1.1. **Understanding the problem and solution** $\quad\quad$ 1.1.1. [[Confounding by intention]] $\quad$ 1.2. **Solving the problem using the IV pattern** $\quad\quad$ 1.2.1. [[Data and conceptual model]] $\quad\quad$ 1.2.2. [[Statistical modeling of IV]] $\quad\quad\quad$ 1.2.2.1. [[Replication of IV in Python]] $\quad\quad$ 1.2.3. [[(Double) Machine learning using IV]] $\quad\quad$ 1.2.4. [[IV the Bayesian way]] $\quad\quad$ 1.2.5. [[Key takeaways]] $\quad$ 1.3. **Oh my the IV!** $\quad\quad$ 1.3.1. [[Oh my! Different standard errors everywhere]] $\quad\quad$ 1.3.2. [[Oh my! Larger effect size in the IV model]] $\quad\quad$ 1.3.3. [[Oh my! Within R-squared in the IV model]] $\quad\quad$ 1.3.4. [[Oh my! Priors in the Bayesian IV model]] $\quad$ 1.4. **Cases using the design pattern** $\quad\quad$ 1.4.1. [[Causal impact of recommendations on Amazon]] $\quad$ 1.5. **Where to from here** $\quad\quad$ 1.5.1. [[Data centricity in the IV pattern]] $\quad\quad$ 1.5.2. [[Using LLMs for IV discovery and data]] 2. Design Pattern II - Regression Discontinuity (RD) $\quad$ 2.1. Understanding the problem and solution $\quad$ 2.2. Solving the problem using the RD pattern $\quad\quad$ 2.2.1. Data and conceptual model $\quad\quad$ 2.2.2. Statistical modeling of RD $\quad\quad$ 2.2.3. Machine learning using RD $\quad\quad$ 2.2.4. RD the Bayesian way 3. Design Pattern III - Matching $\quad$ 3.1. Understanding the problem and solution $\quad$ 3.2. ... 4. To be continued... > [!info]- Last updated: February 3, 2025