%Rikus point: other papers do not really explain why decisions help in predictions
In this paper we considered the overall setting as formulated by~\citet{lakkaraju2017selective}, and building on~\cite{Jung2,mccandless2007bayesian,dearteaga2018learning}, showed that causally informed counterfactual imputation can achieve accurate results.
In this paper we considered the overall setting as formulated by~\citet{lakkaraju2017selective}, and showed that causally informed counterfactual imputation can achieve accurate results.
%PERHAPS WE DONT NEED TIHS
% building on~\cite{Jung2,mccandless2007bayesian,dearteaga2018learning},
%In addition to Lakkaraju et al.~\citet{lakkaraju2017selective} which we build upon, several papers consider related problems to ours.
Note that our setting allowing for unobserved confounding does not fulfill the ignorability or missing at random (MAR) conditions often assumed when processing missing data, biasing any methods built on these assumptions~\cite{lakkaraju2017selective,DBLP:conf/icml/DudikLL11,bang2005doubly,little2019statistical}.
