Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference

We provide a critical review on calibration methods developed in three different areas: survey sampling, missing data analysis and causal inference. We highlight the connections and variations of calibration techniques used in missing data analysis and causal inference to conventional calibration weighting and estimation in survey sampling and provide a common framework through model-calibration and empirical likelihood to unify different calibration methods proposed in recent literature. The goal is to demonstrate the success and effectiveness of calibration methods in achieving some highly desired properties for missing data analysis and causal inference.