groceryheist/ml_measurement_error_overleaf
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anonymize and remove latex comments

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Nathan TeBlunthuis
2023-03-07 15:36:34 -08:00
parent 75e06821cf
commit d259908c97
3 changed files with 36 additions and 437 deletions
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bayesnets.tex
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@@ -16,19 +16,12 @@
\node[observed] (y) {$Y$};
\node[unobserved, above=of y] (x) {$X$};
\node[observed, left=of x] (w) {$W$};
% \node[unobserved, above=of w] (k) {$K$};
\node[observed,right=of x] (z) {$Z$};
% \node[residual,below=of y] (e) {$\varepsilon$};
% \node[residual,below=of w] (xi) {$\xi$};
\draw[-] (z) to (y);
\draw[-] (z) -- (x);
\draw[-] (x) -- (y);
\draw[-] (x) -- (w);
% \draw[-] (y) -- (w);
% \draw[-] (x) -- (xi);
% \draw[-] (w) -- (xi);
\end{tikzpicture}
\caption{In \emph{Simulation 1a}, classifications $W$ are conditionally independent of $Y$ so a model using $W$ as a proxy for $X$ has non-differential error. \label{fig:simulation.1a}}
@@ -41,25 +34,18 @@
\node[observed] (y) {$Y$};
\node[unobserved, above=of y] (x) {$X$};
\node[observed, left=of x] (w) {$W$};
% \node[unobserved, above=of w] (k) {$K$};
\node[observed,right=of x] (z) {$Z$};
% \node[residual,below=of y] (e) {$\varepsilon$};
% \node[residual,below=of w] (xi) {$\xi$};
\draw[-] (z) to (y);
\draw[-] (z) -- (x);
\draw[-] (x) -- (y);
\draw[-] (x) -- (w);
% \draw[-] (k) -- (w);
\draw[-] (x) to (y);
\draw[-] (w) -- (y);
% \draw[-] (x) -- (xi);
% \draw[-] (z) -- (xi);
% \draw[-] (w) -- (xi);
\end{tikzpicture}
\caption{In \emph{Simulation 1b}, the edge from $W$ to $Y$ signifies that the automatic classifications $W$ are not conditionally independent of $Y$ given $X$, indicating differential error.
\label{fig:simulation.1b}
@@ -75,16 +61,12 @@
\node[observed, above=of y] (x) {$X$};
\node[observed, right=of y] (w) {$W$};
% \node[unobserved, above=of w] (k) {$K$};
\node[observed,right=of x] (z) {$Z$};
% \node[residual,below=of y] (e) {$\varepsilon$};
% \node[residual,below=of w] (xi) {$\xi$};
\draw[-] (z) to (y);
\draw[-] (x) -- (y);
\draw[-] (y) -- (w);
\draw[-] (x) -- (z);
% \draw[-] (k) -- (w);
% \draw[-] (w) -- (xi);
\end{tikzpicture}
\caption{In \emph{Simulation 2a}, an unbiased classifier measures the outcome. \label{fig:simulation.2a}}
@@ -97,19 +79,13 @@
\node[observed={white}{gray!40}, above=of y] (x) {$X$};
\node[observed, right=of y] (w) {$W$};
% \node[unobserved, above=of w] (k) {$K$};
\node[observed,right=of x] (z) {$Z$};
% \node[residual,below=of y] (e) {$\varepsilon$};
% \node[residual,below=of w] (xi) {$\xi$};
\draw[-] (x) -- (y);
\draw[-] (z) -- (w);
\draw[-] (y) -- (w);
\draw[-] (x) -- (z);
% \draw[-] (k) -- (w);
\draw[-] (z) -- (y);
% \draw[-] (z) -- (k);
% \draw[-] (y) -- (xi);
% \draw[-] (w) -- (xi);
\end{tikzpicture}
\caption{In \emph{Simulation 2b}, the edge connecting $W$ and $Z$ signifies that the predictions $W$ are not conditionally independent of $Z$ given $Y$, indicating systematic misclassification. \label{fig:simulation.2b}}
\end{subfigure}
@@ -120,7 +96,6 @@
\matrix [draw, below, font=\small, align=center, column sep=2\pgflinewidth, inner sep=0.4em, outer sep=0em, nodes={align=center, anchor=center}] at (current bounding box.south){
\node[observed,label=right:observed] {}; \\
\node[unobserved,label=right:automatically classified]{}; \\
% \node[residual,label=right:error term]{}; \\
};
\end{tikzpicture}
\end{subfigure}