Monthly Archives: February 2016

Congratulations Olivia Perret!

Congratulations to Olivia Perret upon her successful defense of her Masters thesis research, “A novel post-hoc matching procedure using statistical learning methods”.  She is now the 5th student in the Analytics Lab to obtain a Masters of Science in Industrial and Systems Engineering from the University of Oklahoma.

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Olivia’s friends throw her a surprise party in the lab

On February 2, 2016, Olivia presented and defended her work  in front of committee members, Dr. Charles Nicholson, Dr. Hank Grant, and Dr. Suleyman Karabuk.  The committee is thoroughly impressed with the quality of her research, presentation, and mastery of the subject.   Her work builds on and improves a long history of research relating to quantitative analysis for observational (non-randomized) studies.  The novel technique she proposes produces more accurate results than even the most recent advances in the field.

A novel post-hoc matching procedure using statistical learning methods

In this thesis, a statistical learning method is leveraged to create a novel measure for conducting post-hoc matching between a treatment group and a candidate set. Post-hoc matching is a necessary element in many non-random observational studies and arises in diverse fields such as economics, medicine, marketing, and others.

Post-hoc matching has been in use for many years and different methods have been used. A common measure to match the two groups, called the propensity score, can be estimated in a variety of ways. A recent method to estimate it was introduced in 2013 using random forests.

The method introduced in this work utilizes random forest to develop an alternative measure to the propensity score. The new measure, proximity matrix method, is intuitive and potentially captures more similarities between subjects. In order to compare the propensity score method with the novel post-hoc matching method, data sets are generated which logically reflect observational studies with various assumptions regarding treatment selection. Experiments are conducted to evaluate the average treatment effect between the treatment and the control group that are matched. The empirical analysis shows promising results for the proximity matrix method. In particular, the technique has superior results when the treatment selection is made using complex rules, namely, a non-linear model.

This study demonstrates significant potential of the novel method for both researchers and practitioners interested in matching candidates to a test set to estimate the average treatment effect within an observational study when there is an unknown, and possibly complex multivariate relationship with the initial treatment selection.

Congratulations again to Olivia on her excellent work!