We propose and discuss a Bayesian procedure to estimate the average trea...
In this work, we examine recently developed methods for Bayesian inferen...
Causal inference on populations embedded in social networks poses techni...
Forecasting recruitments is a key component of the monitoring phase of
m...
To achieve the goal of providing the best possible care to each patient,...
The sequential treatment decisions made by physicians to treat chronic
d...
There has been significant attention given to developing data-driven met...
Data-driven methods for personalizing treatment assignment have garnered...
We study Bayesian approaches to causal inference via propensity score
re...
Adaptive approaches, allowing for more flexible trial design, have been
...
In the statistical literature, a number of methods have been proposed to...
Analyses of environmental phenomena often are concerned with understandi...
Dynamic treatment regimes (DTR) are a statistical paradigm in precision
...
In the management of most chronic conditions characterized by the lack o...
In studying the marginal effect of antidepressants on body mass index us...
Causal inference of treatment effects is a challenging undertaking in it...
Respondent-Driven Sampling (RDS) is a variant of link-tracing sampling
t...
Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a
s...
The notion of exchangeability has been recognized in the causal inferenc...
Respondent-Driven Sampling (RDS) is a variant of link-tracing, a samplin...