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Hennessy, Jonathan Philip

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Jonathan Philip

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Hennessy, Jonathan Philip
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    Topics in experimental and tournament design
    (2014-10-21) Hennessy, Jonathan Philip; Dasgupta, Tirthankar; Glickman, Mark; Dasgupta, Tirthankar; Glickman, Mark; Miratrix, Luke
    We examine three topics related to experimental design in this dissertation. Two are related to the analysis of experimental data and the other focuses on the design of paired comparison experiments, in this case knockout tournaments. The two analysis topics are motivated by how to estimate and test causal effects when the assignment mechanism fails to create balanced treatment groups. In Chapter 2, we apply conditional randomization tests to experiments where, through random chance, the treatment groups differ in their covariate distributions. In Chapter 4, we apply principal stratification to factorial experiments where the subjects fail to comply with their assigned treatment. The sources of imbalance differ, but, in both cases, ignoring the imbalance can lead to incorrect conclusions. In Chapter 3, we consider designing knockout tournaments to maximize different objectives given a prior distribution on the strengths of the players. These objectives include maximizing the probability the best player wins the tournament. Our emphasis on balance in the other two chapters comes from a desire to create a fair comparison between treatments. However, in this case, the design uses the prior information to intentionally bias the tournament in favor of the better players.
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    A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments
    (Walter de Gruyter GmbH, 2016) Hennessy, Jonathan Philip; Dasgupta, Tirthankar; Miratrix, Luke; Pattanayak, Cassandra; Sarkar, Pradipta
    We consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the observed test statistic to the null distribution of the test statistic conditional on the observed covariate imbalance. We prove that the conditional randomization test has the correct significance level and introduce original notation to describe covariate balance more formally. Through simulation, we verify that conditional randomization tests behave like more traditional forms of covariate adjustmet but have the added benefit of having the correct conditional significance level. Finally, we apply the approach to a randomized product marketing experiment where covariate information was collected after randomization.