Sharpening the BLADE: Missing Data Imputation Using Supervised Machine Learning

Abstract

Incomplete data are quite common which can deteriorate statistical inference, often affecting evidence-based policymaking. A typical example is the Business Longitudinal Analysis Data Environment (BLADE), an Australian Government’s national data asset. In this paper, motivated by helping BLADE practitioners select and implement advanced imputation methods with a solid understanding of the impact different methods will have on data accuracy and reliability, we implement and examine performance of data imputation techniques based on 12 machine learning algorithms. They range from linear regression to neural networks. We compare the performance of these algorithms and assess the impact of various settings, including the number of input features and the length of time spans. To examine generalisability, we also impute two features with distinct characteristics. Experimental results show that three ensemble algorithms: extra trees regressor, bagging regressor and random forest consistently maintain high imputation performance over the benchmark linear regression across a range of performance metrics. Among them, we would recommend the extra trees regressor for its accuracy and computational efficiency.