@@ -22,9 +22,9 @@ data recorded under some policy, the goodness of some other policies \cite{Jung2
Mc-Candless et al. perform Bayesian sensitivity analysis while taking into account latent confounding~\cite{mccandless2007bayesian,mccandless2017comparison}.
\citet{kallus2018confounding} obtain improved policies from data possibly biased by a baseline policy.
The importance in-detail causal modeling and evaluating counterfactual outcomes, as observed also here, is particularly prominent in recent work on fairness of automatic decision making~\cite{DBLP:conf/icml/NabiMS19,DBLP:conf/icml/Kusner0LS19,coston2020counterfactual,madras2019fairness,corbett2017algorithmic,DBLP:journals/jmlr/BottouPCCCPRSS13,DBLP:conf/icml/NabiMS19,DBLP:conf/icml/JohanssonSS16}.
The importance in-detail causal modeling and evaluating counterfactual outcomes, as observed also here, is particularly prominent in recent work on fairness of automatic decision making~\cite{DBLP:conf/icml/NabiMS19,DBLP:conf/icml/Kusner0LS19,coston2020counterfactual,madras2019fairness,corbett2017algorithmic,DBLP:journals/jmlr/BottouPCCCPRSS13,DBLP:conf/icml/NabiMS19,DBLP:conf/icml/JohanssonSS16}. Also identifiability questions in the presence of selection bias or missing data mechanisms require detailed causal modeling~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical}.
%To properly assess decision procedures for their performance and fairness we need to understand the causal relations
Finally, more applied work can be found for example in~\cite{murder,tolan2019why,kleinberg2018human,chouldechova2017fair,brennan2009evaluating}.
Finally, more applied work on automated decision making and risk scoring, related in particular to recidivism, can be found for example in~\cite{murder,tolan2019why,kleinberg2018human,chouldechova2017fair,brennan2009evaluating}.
