The thesis introduces the Local Covariance Measure test for conditional local independence, the Debiased Outcome-adapted Propensity Estimator for efficient covariate adjustment, and the Aalen Covariance Measure for assumption-lean Aalen regression.
BoXHED2.0: Scalable boosting of dynamic survival analysis
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abstract
Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.
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Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis)
The thesis introduces the Local Covariance Measure test for conditional local independence, the Debiased Outcome-adapted Propensity Estimator for efficient covariate adjustment, and the Aalen Covariance Measure for assumption-lean Aalen regression.