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Title: Using Big Data to Sharpen Design-Based Inference in A/B Tests
Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data–often high dimensional and longitudinal–to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a machine learning model to the historical data predicting students’ outcomes as a function of their covariates. Then, use that model to predict the outcomes of the randomized students in the A/B test. Finally, use design-based methods to estimate the treatment effect in the A/B test, using prediction errors in place of outcomes. This method retains all of the advantages of design-based inference, while, under certain conditions, yielding more precise estimators. This paper will give a theoretical condition under which the method improves statistical precision, and demonstrates it using a deep learning algorithm to help estimate effects in a set of experiments run inside ASSISTments.  more » « less
Award ID(s):
1724889
PAR ID:
10095366
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the Eleventh International Conference on Educational Data Mining
Page Range / eLocation ID:
479-485
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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