Author: Zhenhua Wang, Olanrewaju Akande, Jason Poulos and Fan Li
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison:
- household: https://www2.census.gov/programs-surveys/acs/data/pums/2019/1-Year/
- spam: https://archive.ics.uci.edu/ml/datasets/Spambase
- letter: https://archive.ics.uci.edu/ml/datasets/Letter+Recognition
- breast: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
- credit: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
- news: https://archive.ics.uci.edu/ml/datasets/online+news+popularity
- Use sampler.py to create samples with MCAR.
- Use main.py to impute the missing dataset.
- To evaluate the performance of missing imputation, we first need to calculate the estimands in the poputaion dataset, the complete sample dataset and the imputed data using evaluation/calculate_estimands.py. Next, we use evaluation/evaluate_estimands.py to calcaute the performance metrics.
- To display the performance metrics, we use plot_figures.py and plot_tables.py.