1 Introduction  
1.1 2016 U.S. Presidential Election Polling  
2 Background and Setup  
2.1 Notation and Estimands  
2.2 Review: Raking and Post-Stratification  
3 Multilevel Calibration: Approximate Post-Stratification  
3.1 Estimation Error  
3.2 Multilevel Calibration  
4 Double Regression with Post-Stratification (DRP)  
4.1 Using an Outcome Model for Bias Correction  
4.2 Bias Reduction and Asymptotic Normality  
4.3 Choosing an Outcome Model in MRP and DRP  
5 Comparison to Inverse Propensity Score Weighting via Multilevel Modelling  
5.1 Dual Relation to Multilevel Non-Response Modelling  
5.2 The Role of Regularization  
6 Simulation Study Calibrated to the 2016 U.S. Presidential Election  
7 2016 U.S. Presidential Election Polls  
8 Discussion