3 research outputs found
antGLasso: An Efficient Tensor Graphical Lasso Algorithm
The class of bigraphical lasso algorithms (and, more broadly,
'tensor'-graphical lasso algorithms) has been used to estimate dependency
structures within matrix and tensor data. However, all current methods to do so
take prohibitively long on modestly sized datasets. We present a novel
tensor-graphical lasso algorithm that analytically estimates the dependency
structure, unlike its iterative predecessors. This provides a speedup of
multiple orders of magnitude, allowing this class of algorithms to be used on
large, real-world datasets.Comment: 9 pages (21 including supplementary material), 8 figures, submitted
to the GLFrontiers workshop at NeurIPS 202
Causal Inference under Data Restrictions
This dissertation focuses on modern causal inference under uncertainty and
data restrictions, with applications to neoadjuvant clinical trials,
distributed data networks, and robust individualized decision making.
In the first project, we propose a method under the principal stratification
framework to identify and estimate the average treatment effects on a binary
outcome, conditional on the counterfactual status of a post-treatment
intermediate response. Under mild assumptions, the treatment effect of interest
can be identified. We extend the approach to address censored outcome data. The
proposed method is applied to a neoadjuvant clinical trial and its performance
is evaluated via simulation studies.
In the second project, we propose a tree-based model averaging approach to
improve the estimation accuracy of conditional average treatment effects at a
target site by leveraging models derived from other potentially heterogeneous
sites, without them sharing subject-level data. The performance of this
approach is demonstrated by a study of the causal effects of oxygen therapy on
hospital survival rates and backed up by comprehensive simulations.
In the third project, we propose a robust individualized decision learning
framework with sensitive variables to improve the worst-case outcomes of
individuals caused by sensitive variables that are unavailable at the time of
decision. Unlike most existing work that uses mean-optimal objectives, we
propose a robust learning framework by finding a newly defined quantile- or
infimum-optimal decision rule. From a causal perspective, we also generalize
the classic notion of (average) fairness to conditional fairness for individual
subjects. The reliable performance of the proposed method is demonstrated
through synthetic experiments and three real-data applications.Comment: PhD dissertation, University of Pittsburgh. The contents are mostly
based on arXiv:2211.06569, arXiv:2103.06261 and arXiv:2103.04175 with
extended discussion