microimpute 2.0.3

Benchmarking imputation methods for microdata

Microimpute

Microimpute is a Python package for imputing variables from one survey dataset onto another. It wraps five imputation methods behind a common interface so you can benchmark them on your data and pick the one that works best, rather than defaulting to a single approach.

Methods

• Statistical Matching: distance-based matching to find similar donor observations
• Ordinary Least Squares (OLS): linear regression imputation
• Quantile Regression: models conditional quantiles instead of the conditional mean
• Quantile Regression Forests (QRF): non-parametric, tree-based quantile estimation
• Mixture Density Networks (MDN): neural network with a Gaussian mixture output

Autoimpute

The autoimpute function tunes hyperparameters, runs cross-validation across all five methods, and selects the best performer based on quantile loss (for numerical targets) or log loss (for categorical targets). It handles numerical, categorical, and boolean variables.

API

All models follow a fit() / predict() interface. The package supports sample weights to account for survey design, and validates inputs automatically. Adding a custom imputation method is straightforward since new models just need to implement the same interface.

Documentation and paper

• Documentation with examples and interactive notebooks
• Paper presenting microimpute and demonstrating it for SCF-to-CPS net worth imputation

Dashboard

An interactive dashboard for exploring imputation results is available at https://microimpute-dashboard.vercel.app/. It supports file upload, URL loading, direct GitHub artifact integration, and sample data.

Installation

pip install microimpute

For image export (PNG/JPG):

pip install microimpute[images]

Contributing

Pull requests are welcome. If you find a bug or have a feature idea, open an issue or submit a PR.