From 2adce82aa21497ed2a52cd1269dfb5faa276cd25 Mon Sep 17 00:00:00 2001
From: Antti Hyttinen <ajhyttin@gmail.com>
Date: Tue, 6 Aug 2019 15:36:48 +0300
Subject: [PATCH] Title suggestion.

---
 paper/sl.tex | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/paper/sl.tex b/paper/sl.tex
index e5fd50a..2f3e2dc 100755
--- a/paper/sl.tex
+++ b/paper/sl.tex
@@ -42,7 +42,9 @@
 \newcommand{\rcomment}[1]{{{\color{red} [R: #1]}}}
 \newcommand{\mcomment}[1]{{{\color{blue} [M: #1]}}}
 
-\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}
 
 \input{macros}
 \usepackage{chato-notes}
@@ -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.}
 
 \setcounter{section}{1}
 
-- 
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