Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few
preventive or curative treatments available. Modern technology developments of high …

Background: Causal structure learning refers to a process of identifying causal structures
from observational data, and it can have multiple applications in biomedicine and health …

Estimating individual treatment effect (ITE) from observational data is a crucial but
challenging task. Disentangled representations have been used to separate proxy variables …

This chapter is accompanied by survlearners, a package that provides well-documented
implementations of the conditional average treatment effects (CATE) estimation strategies …

Alzheimer's disease and related dementias (ADRD) is a multifactorial disease that involves
several different etiologic mechanisms with various comorbidities. There is also significant …

Developing drugs for treating Alzheimer's disease has been extremely challenging and
costly due to limited knowledge of underlying mechanisms and therapeutic targets. To …

Alzheimer's disease (AD) patients have varying responses to AD drugs and there may be no
single treatment for all AD patients. Trial after trial shows that identifying non-responsive and …

In policy learning, the goal is typically to optimize a primary performance metric, but other
subsidiary metrics often also warrant attention. This paper presents two strategies for …

Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and
costly due to limited knowledge on underlying biological mechanisms and therapeutic …

We consider the stochastic contextual bandit problem in the PAC setting. Fix a distribution ν
over a potentially countable 1 set of contexts C. The action space is A, and for computational …