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” …

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 …

Vandenbroucke et al. 1's critique of the potential outcomes framework does in fact set up a
'straw man': the 'Restricted Potential Outcomes Approach', a term not used by anyone but …

Methods for causal inference have experienced tremendous recent growth; this paper
introduces key concepts underlying causal effect methods in epidemiologic …

Abstract As noted by Wesley Salmon and many others, causal concepts are ubiquitous in
every branch of theoretical science, in the practical disciplines and in everyday life. In the …

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

This chapter explores the idea that causal inference is warranted if and only if the
mechanism underlying the inferred causal association is identified. This mechanistic stance …

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 …

The practice of epidemiology requires asking causal questions. Formal frameworks for
causal inference developed over the past decades have the potential to improve the rigor of …

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