The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …

Studies intended to estimate the effect of a treatment, like randomized trials, may not be
sampled from the desired target population. To correct for this discrepancy, estimates can be …

Causal inference with observational data has generally proceeded under the assumption of
conditional exchangeability. That is, the action (eg, treatment, exposure, intervention) is …

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 …

In 2023, Martinez et al. examined trends in the inclusion, conceptualization,
operationalization and analysis of race and ethnicity among studies published in US …

Iterated conditional expectation (ICE) g‐computation is an estimation approach for
addressing time‐varying confounding for both longitudinal and time‐to‐event data. Unlike …

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new
therapies, there are limitations regarding what comparisons can be made directly from trial …

I read with great interest the recent article by Richardson, Dukes, and Tchetgen Tchetgen
(RDTT hereafter) on their bespoke instrumental variable (BSIV) approach to estimate the …