Observational databases are often used to study causal questions. Before being granted
access to data or funding, researchers may need to prove that “the statistical power of their …

BACKGROUND: Two recent accounts of the use of causal criteria make opposite claims: that
criteria should be used more often to avoid bias in assessments of weak associations and, in …

I present an overview of two methods controversies that are central to analysis and
inference: That surrounding causal modeling as reflected in the “causal inference” …

A risk ratio or difference from a meta-analysis is as many as ten steps away from the
unobservable causal risk ratios and differences in target populations. The steps are like …

Causal inference is a core task of science. However, authors and editors often refrain from
explicitly acknowledging the causal goal of research projects; they refer to causal effect …

Two recent articles, one by Vandenbroucke, Broadbent and Pearce (henceforth VBP) 1 and
the other by Krieger and Davey Smith (henceforth KDS), 2 criticize what these two sets of …

I would like to structure my remarks on the papers by Vandenbroucke et al. 1 and by Krieger
and Davey Smith 2 around a series of propositions that articulate my views concerning …

Observational studies aiming to estimate causal effects often rely on conceptual frameworks
that are unfamiliar to many researchers and practitioners. We provide a clear, structured …

Three assumptions sufficient to identify the average causal effect are consistency, positivity,
and exchangeability (ie,“no unmeasured confounders and no informative censoring,” or …

Population health researchers from different fields often address similar substantive
questions but rely on different study designs, reflecting their home disciplines. This is …