From cf7aefa61f123b7692006a3d0c48ca75dc43b77a Mon Sep 17 00:00:00 2001
From: Antti Hyttinen <ajhyttin@gmail.com>
Date: Wed, 7 Aug 2019 11:39:56 +0300
Subject: [PATCH] ...

---
 paper/sl.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paper/sl.tex b/paper/sl.tex
index 49dfe2f..8c815e9 100755
--- a/paper/sl.tex
+++ b/paper/sl.tex
@@ -363,7 +363,7 @@ We sampled $N=50k$ samples of  $X$, $Z$, and $W$ as independent standard Gaussia
 % It can be / was modified by changing the outcome producing mechanism. For other experiments we changed the outcome generating mechanism so that the outcome was assigned value 1 if
 
 The \emph{default} decision maker in the data fits a logistic regression model $Y \sim  \invlogit(\beta_xx+\beta_zz)$ using the training set. The decisions were assigned by computing the quantile the subject belongs to. The quantile was obtained as the inverse cdf of ... . 
- $T=1$ to $R$ percent of subjects given by the leniency with highest probability of $Y=1$ in the test set. 
+ $T=1$ to $R$ percent of subjects given by the leniency with highest probability of $Y=1$ in the test set. For all subjects for which $T=0$ we set $Y=1$.
  
  
  We used a number of different decision mechanism. A \emph{limited} works as the default but uses regression model $Y \sim  \invlogit(\beta_xx)$. Hence it is unable to observe $Z$. 
-- 
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