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%We initiate the study of mechanisms for \emph{experimental design}.
In the classical {\em experimental design} setting,
-an experimenter \E\ with a budget $B$ has access to a population of $n$ potential experiment subjects $i\in 1,\ldots,n$, each associated with a vector of features $x_i\in\reals^d$ as well as a cost $c_i>0$.
-Conducting an experiment with subject $i$ reveals an unknown value $y_i\in \reals$ to \E. \E\ typically assume some
+an experimenter \E\ with a budget $B$ has access to a population of $n$ potential experiment subjects $i\in \{1,\ldots,n\}$, each associated with a vector of features $x_i\in\reals^d$ as well as a cost $c_i>0$.
+Conducting an experiment with subject $i$ reveals an unknown value $y_i\in \reals$ to \E. \E\ typically assumes some
hypothetical relationship between $x_i$'s and $y_i$'s, \emph{e.g.}, $y_i \approx \T{\beta} x_i$, and estimates
$\beta$ from experiments.
%conducting the experiments and obtaining the measurements $y_i$ allows
%\E\ can estimate $\beta$.
-\E\ 's goal is to select which experiments to conduct, subject to her budget constraint.
+\E 's goal is to select which experiments to conduct, subject to her budget constraint.
%, to obtain the best estimate possible for $\beta$.
We initiate the study of mechanisms for experimental design. In this setting,