Bunch of refs. I can take out if we run out of space.

paper/biblio.bib

@@ -6,6 +6,42 @@
 
 %% Saved with string encoding Unicode (UTF-8) 
 
+@inproceedings{Bareinboim2014:selectionbias,
+  title = {Recovering from Selection Bias in Causal and Statistical Inference},
+  author = {Bareinboim, E. and Tian, J. and Pearl, J.},
+  booktitle = {Proceedings of the 28th AAAI Conference on Neural Information Processing Systems},
+  year = {2014}
+}
+
+@inproceedings{Shpitser2015,
+  title = {Missing Data as a Causal and Probabilistic Problem},
+  author = {Shpitser, I. and Mohan, K. and Pearl, J.},
+  editor = {Marina Meila and Tom Heskes},
+  booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence},
+  publisher = {AUAI Press},
+  pages = {802--811},
+  year = {2015}
+}
+
+@inproceedings{Mohan2013,
+author = {K. Mohan and J. Pearl and J. Tian},
+title = {Graphical models for inference with missing data},
+booktitle = {Advances in Neural Information Systems},
+volume = {26},
+pages = {1277--1285},
+year = {2013}
+}
+
+@incollection{smr1999,
+author={Peter Spirtes and Christopher Meek and Thomas Richardson},
+title={An Algorithm for Causal Inference in the Presence of Latent Variables and Selection Bias},
+booktitle={Computation, Causation \& Discovery},
+editor = {Clark Glymour and Gregory F. Cooper},
+year = {1999},
+pages = {211--252},
+publisher = { AAAI / MIT Press}
+}
+
 @article{holland1986statistics,
   title={Statistics and causal inference},
   author={Holland, Paul W},

paper/introduction.tex

@@ -69,7 +69,7 @@ Settings where some variables are not observed for all samples have \emph{missin
 
 
 %WE WANT TO CITE HERE ALL SELECTIVE LABELS PAPERS TO SELL THIS VIEWPOINT
-Recently, \citet[KDD2017]{lakkaraju2017selective} referred to the problem of evaluation is such settings as the '{\it selective labels problem}' empahasizing the fact that outcomes in the data are selectively labeled based on the decisions
+Recently, \citet[KDD'17]{lakkaraju2017selective} referred to the problem of evaluation is such settings as the '{\it selective labels problem}' empahasizing the fact that outcomes in the data are selectively labeled based on the decisions
 (also considered by~\cite{dearteaga2018learning,kleinberg2018human}).
 %
 \citet{lakkaraju2017selective} also presented {\it contraction}, a method for evaluating decision making mechanisms in a setting where subjects are randomly assigned to decision makers with varying leniency levels. 
@@ -80,7 +80,7 @@ We note, however, that for contraction to work, we need lenient decision makers
 % THIS BELONGS TO RELATED WORK
 In another recent paper, \citet{Jung2} studied unobserved confounding in the context of creating optimal decision policies. 
 %
-They approached the problem with Bayesian modelling, but they don't consider selective labeling or the possibility that the decisions reflected in the data may be taken by several decision makers with differing levels of leniency.
+They approached the problem with Bayesian modelling and sensitivity analysis, but they don't consider selective labeling or the possibility that the decisions reflected in the data may be taken by several decision makers with differing levels of leniency.
 
 \spara{Our contributions}
 In this paper, we build upon the problem setting used in~\citet{lakkaraju2017selective} and present a novel, modular framework to evaluate decision makers over selectively labeled data. 
@@ -95,5 +95,7 @@ The results indicate that our method achieves more accurate results with conside
 
 %\todo{Michael}{Summarize our experimental results}
 
-
+%selection bias Bareinboim2014:selectionbias
+%latent variables, selection bias smr1999
+%missing data, Mohan2013 Shpitser2015
 

paper/related.tex

@@ -22,7 +22,7 @@ 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}. Also identifiability questions in the presence of selection bias or missing data mechanisms require detailed causal modeling~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical}.
+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}. Several author study selection bias or missing data in the context of identifiability of causal effects and causal structure~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical,Bareinboim2014:selectionbias,smr1999,Mohan2013,Shpitser2015}.
 %To properly assess decision procedures for their performance and fairness we need to understand the causal relations 
 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}.