Some of the outcome data in my data set are missing. I believe that the missing data mechanism is missing at random (MAR) as the observed characteristics significantly differ between the missing and non-missing data but there is no theoretical background supporting that there are unobserved factors that can determine if the data is missing or not.
I want to drop the missing observations instead of using an imputation method. However, I am not sure how to prevent bias. I can control for the factors that explain if the data is missing or not simply by adding them into regression but I am not sure if this would be enough.