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    Causal Survival Analysis – Machine Learning Assisted Models: Structural Nested Accelerated Failure Time Model and Threshold Regression
    (2022) Chen, Yiming; Lee, Mei-Ling ML; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Time-varying confounding for intervention complicates causal survival analysis when the data are collected in a longitudinal manner. Traditional survival models that only adjust for time-dependent covariates provide a biased causal conclusion for the intervention effect. Some techniques have been developed to address this challenge. Nevertheless, these existing methods may still lack power, and suffer from computational burden given high dimensional data with a temporally connected nature. The first part of this dissertation focuses on one of the methods that deal with time-varying confounding, the Structural Nested Model and associated G-estimation. Two Neural Networks (GE-SCORE and GE-MIMIC) were proposed to estimate the Structural Nested Accelerated Failure Time Model. The proposed algorithms can provide less biased and individualized intervention causal effect estimation. The second part explored the causal interpretations and applications of the First-Hitting-Time based Threshold Regression Model using a Wiener process. Moreover, a Neural Network expansion of this specific type of Threshold Regression (TRNN) was explored for the first time.
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    Using machine learning to measure the cross section of top quark pairs in the muon+jets channel at the Compact Muon Solenoid
    (2011) Kirn, Malina Aurelia; Hadley, Nicholas; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The cross section for pp to top-antitop production at a center of mass energy of 7 TeV is measured using a data sample with integrated luminosity 36.1 inverse pb collected by the CMS detector at the LHC. The analysis is performed on a computing grid. Events with an isolated muon and three hadronic jets are analyzed using a multivariate machine learning algorithm. Kinematic variables and b tags are provided as input to the algorithm; output from the algorithm is used in a maximum likelihood fit to determine top-antitop event yield. The measured cross section is 151 +/- 15(stat.) +35/-28(syst.) +/- 6(lumi.) pb.