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  • \documentclass[sigconf,anonymous]{acmart}
    % \documentclass[sigconf]{acmart}
    
    
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    \usepackage{tikz}
    \usepackage{tikz-cd}
    \usetikzlibrary{arrows,automata, positioning}
    
    
    % Packages
    \usepackage{type1cm}     % type1 computer modern font
    \usepackage{graphicx}     % advanced figures
    \usepackage{xspace}     % fix space in macros
    \usepackage{balance}     % to better equalize the last page
    \usepackage{multirow}     % multi rows for tables
    \usepackage[font={bf}, tableposition=top]{caption}     % captions on top for tables
    \usepackage{bold-extra}     % bold + {small capital, italic}
    \usepackage{siunitx}          % \num for decimal grouping
    \usepackage[vlined,linesnumbered,ruled,noend]{algorithm2e}     % algorithms
    \usepackage{booktabs}     % nicer tables
    %\usepackage[hyphens]{url}     % handle long urls
    %\usepackage[bookmarks, pdftex, colorlinks=false]{hyperref}     % clickable references
    %\usepackage[square,numbers]{natbib}     % better references
    \usepackage{microtype}    % compress text
    \usepackage{units}     % nicer slanted fractions
    \usepackage{mathtools}     % amsmath++
    %\usepackage{amssymb}     % math symbols
    %\usepackage{amsmath}
    \usepackage{relsize}
    \usepackage{caption}
    \captionsetup{belowskip=6pt,aboveskip=2pt} % to save space.
    %\usepackage{subcaption}
    % \usepackage{multicolumn}
    \usepackage[]{inputenc}
    \usepackage{xfrac}
    \RequirePackage{graphicx,color}
    \usepackage[font={small}]{subfig} % subfig, 4 figures in a row
    \usepackage{pifont}
    \usepackage{footnote} % show footnotes in tables
    \makesavenoteenv{table}
    
    
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    \newcommand{\acomment}[1]{{{\color{orange} [A: #1]}}}
    \newcommand{\rcomment}[1]{{{\color{red} [R: #1]}}}
    \newcommand{\mcomment}[1]{{{\color{blue} [M: #1]}}}
    
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    \newtheorem{problem}{Problem}
    
    
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    \newcommand{\ourtitle}{Evaluating Decision Makers over Selectively Labeled Data}
    
    
    \input{macros}
    
    \usepackage{chato-notes}
    
    
    
    \title{\ourtitle}
    
    \author{Michael Mathioudakis}
    \affiliation{%
      \institution{University of Helsinki}
      \city{Helsinki} 
      \country{Finland} 
    }
    \email{michael.mathioudakis@helsinki.fi}
    
    
    \begin{abstract}
    
    Today, AI systems replace humans in an increasing number of decisions affecting people's lives.
    
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    Therefore, it is important to evaluate the performance of such systems {\it offline}, i.e., before they are deployed in real settings --
    and compare it to the performance of human decisions they aim to replace.
    
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    One major challenge in such cases is that often past decisions have skewed the data on which the evaluation is performed. 
    
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    For example, when a bank decides whether a customer should be granted a loan, it is desired to grant loans to customers who would honor its conditions, but not to ones who would violate them.
    
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    However, we can directly evaluate only the decision to grant the loan, while we cannot observe whether customers who were not granted the loan would indeed violate its conditions. 
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    Such skew appears in the decisions of both human and AI decision makers -- and should be properly taken into account for evaluation.
    
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    In this paper, we develop a Bayesian approach towards this end, using counterfactual-based imputation to infer unobserved outcomes.
    
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    Compared to previous state-of-the-art, the quality of decisions is estimated more accurately and with lower variance. 
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    The approach is also demonstrated to be robust to different variations in the decision mechanisms in the data.
    
    \note{Michael}{On one hand, since we use judicial data in our experiments, it makes sense to use the bail-or-jail case in the abstract. On the other hand, this does not connect with the motivation we provide to evaluate the decision of (computer/ML/AI) systems, since jail-or-bail decisions are not currently made by such systems (risk scores are used as assisting tools). The bank loan example might look better in the abstract.}
    
    \end{abstract}
    
    
    \begin{document}
    
    
    \fancyhead{}
    \maketitle
    
    \renewcommand{\shortauthors}{Authors}
    
    
    
    \input{introduction}
    
    \input{setting}
    
    \input{imputation}
    
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    \input{experiments} 
    
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    \input{related} 
    
    \input{conclusions}
    
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    % \textbf{Acknowledgments.}
    %The computational resources must be mentioned. 
    
    
    %\clearpage
    % \balance
    \bibliographystyle{ACM-Reference-Format}
    \bibliography{biblio}
    %\balancecolumns % GM June 2007
    
    
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    %\input{appendix}
    
    
    \end{document}