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Causal Inference Under Network Interference: A Framework for Experiments on Social Networks
(2017-01-26)
No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect ...
Federated and Transfer Learning with Multi-site Electronic Health Record Data
(2022-05-05)
Electronic health records (EHR) data has become crucial resources for a growing number of data-driven biomedical studies such as automated disease diagnosis and genotype-phenotype translation studies. Nevertheless, power ...
New Methods for Causal Inference in Randomized Experiments and Observational Studies
(2022-05-12)
A fundamental goal of numerous studies across social and health sciences is to estimate the causal effect of an intervention or treatment on an outcome in a well-defined target population. In view of this goal, the ...
Improved Generative Evaluation: Utilizing the Manifold Hypothesis
(2022-03-07)
Reliably diagnosing and evaluating generative models remains an open problem. The most common metric employed to measure the performance of image generating models has been the Fréchet Inception Distance (FID) score. It ...
A Multi-resolution Hard Attention Model to Select Regions of Interest on Whole Pathology Slide Images
(2022-05-23)
With drastic improvements in the performance of neural networks and computer vision algorithms, deep learning-based imaging analysis models have been applied to a wide variety of fields. Along with this expansion, many ...
“Please Respect Our Terms and Conditions”: A Causal Analysis of GDPR Impact on Privacy Policies
(2021-06-04)
The General Data Protection Regulation (GDPR) has been widely praised as the most consequential privacy law in history. However, GDPR causal effects have never been formally analyzed, and all GDPR praises are largely ...
Statistical Methods for the Analysis of Observational Data With Multiple Correlated Outcomes
(2017-09-11)
In this work, we consider three problems in applied statistics motivated by complex datasets, with approaches from both Frequentist and Bayesian paradigms. Chapter 2 is motivated by case-control data collected for the Army ...
Three Essays on Making Casual Inferences with Test Scores
(2021-05-12)
In education research test scores are a common object of analysis. Across studies test
scores can be an important outcome, a highly predictive covariate, or a means of assigning
treatment. However, test scores are a ...
Statistical Methods for Assessing Complex Multi-Exposure Data in HIV and Genetic Epidemiology
(2018-05-16)
Complex, multi-exposure problems arise in many forms. In this dissertation, we delve into three disparate forms of complex, multi-exposure questions, from the safety of combination antiretroviral (ARV) regimens to the ...
A Grand Journey of Statistical Hierarchical Modelling
(2017-05-03)
This thesis presents three research reports composed by the candidate and his collaborators on different perspectives and applications of statistical hierarchical modelling, which seeks to connect the observed quantities ...