Typos tyops.

paper/conclusions.tex

@@ -7,7 +7,7 @@ In this paper we considered the task of evaluating (automated) decision makers,
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 The challenge is that the evaluation often needs to be based on data where present decisions imply selective labeling and missing data, biasing any standard statistical data analysis results.  
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-We showed that with proper causal modeling, automated decision makers can be evaluated even based on such selectively labeled data.
+We showed that with proper causal modelling, automated decision makers can be evaluated even based on such selectively labeled data.
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 Contrary to previous work, our proposed approach allows for more accurate evaluations, with less variation, also in settings that evaluation was not possible before.
 

paper/experiments.tex

@@ -240,7 +240,7 @@ This is refered to as \trueevaluation.
 \subsection{Results}
 \label{sec:results}
 
-These results show the accuracy of the different evaluators (Section~\ref{sec:evaluators}) on different types of automated decision makers \machine over a data sets employing different decision makers \humanset (Section~\ref{sec:decisionmakers}).
+These results show the accuracy of the different evaluators (Section~\ref{sec:evaluators}) on different types of automated decision makers \machine over data sets employing different decision makers \humanset (Section~\ref{sec:decisionmakers}).
 %In these results we evaluate decision maker \machine over data 
 %For the results we describe immediately below, we executed the pipeline for multiple random train-test splits and different leniency levels for \machine.
 

paper/related.tex

@@ -11,7 +11,7 @@ We adopted the overall setting as formulated by~\citet{lakkaraju2017selective},
 %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. 
-The setting allows for unobserved confounding, and so it cannot be addressed with standard methods for processing missing data, which typiclly make strong {\it ignorability} or {\it missing at random (MAR)} conditions~\cite{DBLP:conf/icml/DudikLL11,bang2005doubly,little2019statistical}. % MM: I removed lakkaraju2017selective from the list, we do not want to give the impression that lakkaraju2017selective makes these assumptions
+The setting allows for unobserved confounding, and so it cannot be addressed with standard methods for processing missing data, which typically make strong {\it ignorability} or {\it missing at random (MAR)} conditions~\cite{DBLP:conf/icml/DudikLL11,bang2005doubly,little2019statistical}. % MM: I removed lakkaraju2017selective from the list, we do not want to give the impression that lakkaraju2017selective makes these assumptions
 
 In our simulations we compared in particular to \contraction of~\citet{lakkaraju2017selective}, an approach that is appealing in its simplicity.  
 %