On December 11, 2017, Tobias Aufenanger (Friedrich-Alexander University Erlangen-Nuremberg) will give a talk about "Machine Learning for Prediction Based Stratification in Economic Experiments" in the context of the SFB 901.
Abstract:
This paper proposes a way of using observational pretest data for the design of experiments.
In particular, this paper trains a random forest on the pretest data and stratifies the allocation of
treatments to experimental units on the predicted dependent variables. This approach reduces
much of the arbitrariness involved in defining strata directly on the basis of covariates.
A simulation on 300 random samples drawn from six data sets shows that this algorithm is
extremely effective in reducing the variance of the estimation compared to random allocation
and to traditional ways of stratification. On average, this stratification approach requires half the
sample size to estimate the treatment effect with the same precision as complete randomization.
In more than 80% of all samples the estimated variance of the treatment estimator is lower and
the estimated statistical power is higher than for standard designs such as complete randomization,
conventional stratification or Mahalanobis matching.