Will historical data lead to target leakage? I'm bulding a employee churn model. I've employee data from 2016 to 2019 (of people who stayed/left the company), my goal is to train using data from 2016 to 2018 and predict on 2019.

Since there's people who did not leave the company between 2016-2019 there's alot of repetead employees, so my training set is: data from 2018 about employees who did not leave the company injected with data of employees who left the company in 2016/2017/2018 in order to only have one person once in the training dataset.

My question 


Does having only the persons who left in 2016/2017 lead to target leakage?
I'm not using time dummies but can my model overfit thinking employees are more likely to leave the company in 2016/2017 because it's what it sees more often?
And if so how can I avoid this problem?


Thanks :)!
I'm going to kind of ignore your question. To me it sounds like you have a dataset containing essentially employee starting and 'end dates' or if the employee is still working for you no end date. This sounds like the standard case for a class of regression techniques called 'survival regression' or survival analysis. I suggest you look into this.

Now for your question, Im not too sure about target leakage as I cannot quite see how, if you have a structural change in employees year by year then theres definitely some leakage yeah.