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.

Creator

• Patikorn, Thanaporn

Colaboradores

• Heffernan, Neil

Degree

• PhD

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-050817-105913

Palavra-chave

• treatment effect detection
• Data Mining
• linear regression

Advisor

• Heffernan, Neil

Defense date

• 2017-05-05

Year

• 2017

Date created

• 2017-05-08

Resource type

• Thesis

Rights statement

• In Copyright

Última modificação

• 2021-02-01