Improvements on Trained Across Multiple Experiments (TAME), a New Method for Treatment Effect Detection
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open in viewerOne 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.
- Creator
- Colaboradores
- Degree
- Unit
- Publisher
- Language
- English
- Identifier
- etd-050817-105913
- Palabra Clave
- Advisor
- Defense date
- Year
- 2017
- Date created
- 2017-05-08
- Resource type
- Rights statement
- Última modificación
- 2021-02-01
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Miniatura | Título | Visibilidad | Embargo Release Date | Acciones |
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tpatikorn_ms_thesis.pdf | Público | Descargar |
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