We consider the problem of learning causal structures in sparse high-dimensional settings
that may be subject to the presence of (potentially many) unmeasured confounders, as well …

One of the established approaches to causal discovery consists of combining directed
acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional …

Social isolation has become a growing public health concern in older adults and older
adults with mild cognitive impairment. Coping strategies must be developed to increase …

Questions of cause and effect are central to many areas of scientific research and policy
making. For example, by how much does societal mask-wearing reduce the transmission …

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given
graph in which the properties of the reduced graph are to be induced from the properties of …

Directed graphical models are commonly used to model causal relations between random
variables and to understand conditional independencies in their joint distributions. We focus …

In this dissertation, I study peer review—the process by which scientists evaluate one
another's work for publication or funding—through three distinct but related lenses. I focus …

Causal graphs are essential tools to find sufficient adjustment sets in observational studies.
Subject matter experts can sometimes specify these graphs, but often the dependence …