@@ -269,6 +269,14 @@ We show that our method is robust against violations and modifications in the da
\section{Counterfactual-Based Imputation For Selective Labels}
\acomment{This chapter should be our contributions. One discuss previous results we build over but one should consider putting them in the previous section.}
\acomment{We need to start by noting that with a simple example how we assume this to work. If X indicates a safe subject that is jailed, then we know that (I dont know how this applies to other produces) that Z must have indicated a serious risk. This makes $Y=0$ more likely than what regression on $X$ suggests.}
\acomment{I do not understand what we are doing from this section. It needs to be described ASAP.}
\begin{itemize}
\item Theory \\ (Present here (1) what counterfactuals are, (2) motivation for structural equations, (3) an example or other more easily approachable explanation of applying them, (4) why we used computational methods)
