MicroImpute is a powerful framework that enables variable imputation through a variety of statistical methods. By providing a consistent interface across different imputation techniques, it allows researchers and data scientists to easily compare and benchmark different approaches using quantile loss calculations to determine the method provding most accurate results. Thus, MicroImpute provides two main uses: imputing one or multiple variables with one of the methods available, and comparing and benchmarking different methods to inform a method’s choice.
The framework currently supports the following imputation methods:
- Statistical Matching
- Ordinary Least Squares Linear Regression
- Quantile Regression Forests
- Quantile Regression
This is a work in progress that may evolve over time, including new statistical imputation methods and features.