HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

TL;DR

HyperImpute is a flexible iterative imputation framework that automatically configures models and hyperparameters, combining the strengths of traditional and deep generative methods for accurate missing data imputation.

Contribution

It introduces a generalized, adaptive imputation method with automatic model selection, bridging traditional iterative approaches and deep generative models.

Findings

Achieves accurate imputations comparable to state-of-the-art benchmarks.

Demonstrates robustness and flexibility across diverse datasets.

Provides an extensible implementation with out-of-the-box components.

Abstract

Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability of learning conditional distributions directly, but suffer from the practical requirement for appropriate model specification of each and every variable. On the other hand, recent methods using deep generative modeling benefit from the capacity and efficiency of learning with neural network function approximators, but are often difficult to optimize and rely on stronger data assumptions. In this work, we study an approach that marries the advantages of both: We propose *HyperImpute*, a generalized iterative imputation framework for adaptively and automatically configuring column-wise models and their hyperparameters. Practically, we provide a concrete implementation with out-of-the-box learners, optimizers, simulators, and extensible interfaces. Empirically, we investigate this framework via comprehensive experiments and sensitivities on a variety of public datasets, and demonstrate its ability to generate accurate imputations relative to a strong suite of benchmarks. Contrary to recent work, we believe our findings constitute a strong defense of the iterative imputation paradigm.