Controlling for confounding bias is crucial in causal inference. Causal inference using data
from observational studies (eg, electronic health records) or imperfectly randomized trials …

Studies designed to estimate the effect of an action in a randomized or observational setting
often do not represent a random sample of the desired target population. Instead, estimates …

Research intended to estimate the effect of an action, like in randomized trials, often do not
have random samples of the intended target population. Instead, estimates can be …

Least absolute shrinkage and selection operator (LASSO) regression is widely used for
large-scale propensity score (PS) estimation in health-care database studies. In these …

This paper proposes a novel view in the the philosophy of race & causation literature known
as “causal agnosticism” about race. Causal agnosticism about race implies that it is …

Comparisons of treatments or exposures are of central interest in epidemiology, but direct
comparisons are not always possible due to practical or ethical reasons. Here, we detail a …

Governments and public health authorities use seroprevalence studies to guide responses
to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals …

To estimate the average treatment effect in real-world populations, observational studies are
typically designed around real-world cohorts. However, even when study samples from …

I investigate how race, algorithms, and causality intersect in this dissertation. The first
chapter explores social constructivist viewpoints on race and evaluates Sally Haslanger's …