- Keywords: causal discovery, missing data
- Abstract: The missing data issue is a common phenomenon in many applications such as healthcare. When applying causal discovery algorithms, such as PC, to a data set with missing values, not properly handling the missing data issue might introduce bias and lead to wrong causal relations. In this work, we identify the potential errors of simply applying PC to data sets with missing values. Further, we extend the constraint-based causal discovery method PC to handle binary data sets with missing values for the application Neuropathic Pain diagnosis.