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Improvements on Trained Across Multiple Experiments (TAME), a New Method for Treatment Effect Detection

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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 “prior” 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.

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  • English
Identifier
  • etd-050817-105913
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  • 2017
Date created
  • 2017-05-08
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Dernière modification
  • 2021-02-01

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