Dealing with missing data

This chapter focuses on estimating treatment effects while dealing with missing data. We start with a brief introduction to some missing data concepts. Then, we discuss common methods to handle missing data, including both traditional approaches and those based on machine learning and deep learning approaches. Next, we highlight important factors to consider when analyzing datasets with missing information and the potential risk of confounding bias. To better understand the concepts discussed in this chapter, we use a simulated dataset to assess the impact of treatment interventions on patients with multiple sclerosis.