# Quality control, data cleaning, imputation

arXiv: https://arxiv.org/abs/2110.15877 · HTML: https://mlsanity.com/paper/2110.15877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [Time Series Forecasting Using a Hybrid Deep Learning Method: A Bi-LSTM Embedding Denoising Auto Encoder Transformer](https://mlsanity.com/paper/2509.17165/) (2025)

## Abstract (arXiv)

This chapter addresses important steps during the quality assurance and control of RWD, with particular emphasis on the identification and handling of missing values. A gentle introduction is provided on common statistical and machine learning methods for imputation. We discuss the main strengths and weaknesses of each method, and compare their performance in a literature review. We motivate why the imputation of RWD may require additional efforts to avoid bias, and highlight recent advances that account for informative missingness and repeated observations. Finally, we introduce alternative methods to address incomplete data without the need for imputation.