  In this paper we present tools for applied researchers that re-purpose
off-the-shelf methods from the computer-science field of machine learning to
create a "discovery engine" for data from randomized controlled trials (RCTs).
The applied problem we seek to solve is that economists invest vast resources
into carrying out RCTs, including the collection of a rich set of candidate
outcome measures. But given concerns about inference in the presence of
multiple testing, economists usually wind up exploring just a small subset of
the hypotheses that the available data could be used to test. This prevents us
from extracting as much information as possible from each RCT, which in turn
impairs our ability to develop new theories or strengthen the design of policy
interventions. Our proposed solution combines the basic intuition of reverse
regression, where the dependent variable of interest now becomes treatment
assignment itself, with methods from machine learning that use the data
themselves to flexibly identify whether there is any function of the outcomes
that predicts (or has signal about) treatment group status. This leads to
correctly-sized tests with appropriate $p$-values, which also have the
important virtue of being easy to implement in practice. One open challenge
that remains with our work is how to meaningfully interpret the signal that
these methods find.
