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: impute one or multiple variables with any of the available methods, and compare their performance using quantile loss and log loss.

The package currently supports:

• Hot Deck Matching

• Ordinary Least Squares (OLS) Linear Regression

• Quantile Random Forests (QRF)

• Quantile Regression

• Mixture Density Networks (MDN)

This is a work in progress and may evolve over time with new methods and features.

Microimputation dashboard¶

Users can visualize imputation and benchmarking results at https://microimpute-dashboard.vercel.app/.

To use the dashboard, CSV files must contain the following columns in this exact order:

• type: Type of metric (e.g., “benchmark_loss”, “distribution_distance”, “predictor_correlation”)

• method: Imputation method name (e.g., “QRF”, “OLS”, “QuantReg”, “Matching”, “MDN”)

• variable: Variable being imputed or analyzed

• quantile: Quantile level (numeric value, “mean”, or “N/A”)

• metric_name: Name of the metric (e.g., “quantile_loss”, “log_loss”)

• metric_value: Numeric value of the metric

• split: Data split indicator (e.g., “train”, “test”, “full”)

• additional_info: JSON-formatted string with additional metadata

The format_csv() function from microimpute.utils formats imputation and benchmarking results into the correct structure for the dashboard. It accepts outputs from various analysis functions (autoimpute results, comparison metrics, distribution comparisons) and returns a properly formatted DataFrame.