One of my previous works introduced a new data mining technique to analyze multiple experiments called TAME: Trained Across Multiple Experiments. TAME detects treatment effects of a randomized controlled experiment by utilizing data from outside of the experiment of interest. TAME with linear regression showed promising result; in all simulated scenarios, TAME was at least as good as a standard method, ANOVA, and was significantly better than ANOVA in certain scenarios. In this work, I further investigated and improved TAME by altering how TAME assembles data and creates subject models. I found that mean-centering â€œpriorâ€� data and treating each experiment as equally important allow TAME to detect treatment effects better. In addition, we did not find Random Forest to be compatible with TAME.
Worcester Polytechnic Institute
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Patikorn, Thanaporn, "Improvements on Trained Across Multiple Experiments (TAME), a New Method for Treatment Effect Detection" (2017). Masters Theses (All Theses, All Years). 792.
data mining, treatment effect detection, linear regression