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Double Machine Learning for Time Series A scaffold-astro book
All chapters References

Part I · Foundations

  1. W01 Potential Outcomes and the Frisch-Waugh-Lovell Theorem
  2. W02 Neyman Orthogonality and Double Machine Learning
  3. W03 Comprehensive Validation Framework

Part IV · Integration

  1. W04 Cross-Sectional Application: Price Elasticity with Sensitivity Analysis
  2. W05 Temporal PLR DML for Time Series
  3. W06 Panel DML and Rolling Window Methods
  4. W07 FRED Integration: Macroeconomic Controls

Part V · Synthesis

  1. W08 Competitor Pricing: An Insurance Application
  2. W09 Heterogeneity Analysis
  3. W10 Research Pipeline Utilities for Causal Inference

Double Machine Learning for Time Series

Companion code + manuscript-style material for temporal partially-linear DML, cross-fitting, HAC inference, and synthetic examples.

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