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paper/introduction.tex

@@ -57,13 +57,13 @@ There is a rich literature on problems that arise in similar settings, our speci
 % COUNTERFACTUALS: FUNDAMENTAL PROBLEM
 At its core, our task is to answer a `what-if' question, asking ``what would the outcome have been if a different decision had been made'' (a counterfactual), this is often mentioned as the `fundamental problem' in causal inference~\cite{holland1986statistics, bookofwhy}.
 % SELECTION BIAS
-Settings where data samples are chosen through some intricate filtering mechanism are said to exhibit {\it selection bias} (see, for example, \citet{hernan2004structural}).
+Settings where data samples are chosen through some intricate filtering mechanism are said to exhibit {\it selection bias} (see, for example, \citet{hernan2004structural}). In the present case, any models predicting outcomes can only be on samples where the decision was positive.
 %WHAT WE DO NOT HAVE THIS???
 % MISSING DATA %IMPUTATION 
-Settings where some variables are not observed for all samples have missing data.
+Settings where some variables are not observed for all samples have \emph{missing data}. Here, the outcomes for samples with a negative decision are considered missing, or labeled with some default value.
 %Research on selection bias has achieved results in recovery the structure of the generative model (i.e., the mechanism that results in bias) and estimating causal effects (e.g.,~\citet{pearl1995empirical} and~\citet{bareinboim2012controlling}).
 %OFFLINE POLICY EVALUATION
-Offline policy assessment refers to evaluation of a decision policy over a dataset recorded under another policy~\cite{Jung2}.
+\emph{Offline policy assessment}  refers to evaluation of a decision policy over a dataset recorded under another policy~\cite{Jung2}, which is also the case here, the decision are always based on a particular policy.
 %COUNFOUNDING AND SENSITIVITY ANALYSIS