\newcommand{\ourtitle}{Working title: From would-have-beens to should-have-beens: Counterfactuals in model evaluation}
%\newcommand{\ourtitle}{Working title: From would-have-beens to should-have-beens: Counterfactuals in model evaluation}
\newcommand{\ourtitle}{Evaluating Decision Makers over Selectively Labeled Data}
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\usepackage{chato-notes}
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@@ -199,6 +201,7 @@ The outcome $Y$ is affected by the observed background factors $X$, unobserved
We use a propensity score framework to model $X$ and $Z$: they are assumed continuous Gaussian variables, with the interpretation that they represent summarized risk factors such that higher values denote higher risk for a negative outcome ($Y=0$). Hence the Gaussianity assumption here is motivated by the central limit theorem.
\acomment{Not sure if this is good to discuss here or in the next section: if we would like the next section be full of our contributions and not lakkarajus, we should place it here.}
