  Combining machine learning with econometric analysis is becoming increasingly
prevalent in both research and practice. A common empirical strategy involves
the application of predictive modeling techniques to 'mine' variables of
interest from available data, followed by the inclusion of those variables into
an econometric framework, with the objective of estimating causal effects.
Recent work highlights that, because the predictions from machine learning
models are inevitably imperfect, econometric analyses based on the predicted
variables are likely to suffer from bias due to measurement error. We propose a
novel approach to mitigate these biases, leveraging the ensemble learning
technique known as the random forest. We propose employing random forest not
just for prediction, but also for generating instrumental variables to address
the measurement error embedded in the prediction. The random forest algorithm
performs best when comprised of a set of trees that are individually accurate
in their predictions, yet which also make 'different' mistakes, i.e., have
weakly correlated prediction errors. A key observation is that these properties
are closely related to the relevance and exclusion requirements of valid
instrumental variables. We design a data-driven procedure to select tuples of
individual trees from a random forest, in which one tree serves as the
endogenous covariate and the other trees serve as its instruments. Simulation
experiments demonstrate the efficacy of the proposed approach in mitigating
estimation biases and its superior performance over three alternative methods
for bias correction.
