1. Introduction  
1.1 Model-based imputation  
1.2 Imputation with deep learning models  
2. Missing Data Imputation Methods  
2.1 MICE with classification tree models  
2.2 Generative Adversarial Imputation Network (GAIN)  
2.3 Multiple Imputation using Denoising Autoencoders (MIDA)  
3. Simulation-based evaluation of imputation methods  
4. Evaluation based on ACS  
4.1 The “population” data  
4.2 Simulations with n=10,000  
4.3 Simulations with n=100,000 and 30% MCAR  
4.4 Role of hyperparameters in tree-based MICE  
5. Evaluation based on “benchmark” datasets  
6. Conclusion