Prerequisites for Non-Probability Sampling

Direct prerequisites (4)

1. Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
2. Law of Large Numberslayer 0B, tier 1
3. Central Limit Theoremlayer 0B, tier 1
4. Double/Debiased Machine Learninglayer 3, tier 1

Reachable through the chain (21)

These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.

• Random Variableslayer 0A, tier 1
• Kolmogorov Probability Axiomslayer 0A, tier 1
• Sets, Functions, and Relationslayer 0A, tier 1
• Basic Logic and Proof Techniqueslayer 0A, tier 2
• Common Probability Distributionslayer 0A, tier 1
• Asymptotic Statistics: M-Estimators, Delta Method, LANlayer 0B, tier 1
• Maximum Likelihood Estimation: Theory, Information Identity, and Asymptotic Efficiencylayer 0B, tier 1
• Differentiation in Rnlayer 0A, tier 1
• Vectors, Matrices, and Linear Mapslayer 0A, tier 1
• Continuity in Rⁿlayer 0A, tier 1
• Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
• KL Divergencelayer 1, tier 1
• Information Theory Foundationslayer 0B, tier 2
• Modes of Convergence of Random Variableslayer 0B, tier 1
• Measure-Theoretic Probabilitylayer 0B, tier 1
• Cross-Validation Theorylayer 2, tier 2
• Empirical Risk Minimizationlayer 2, tier 1
• Concentration Inequalitieslayer 1, tier 1
• Bias-Variance Tradeofflayer 2, tier 2
• Causal Inference Basicslayer 3, tier 3
• Hypothesis Testing for MLlayer 2, tier 2