Invited Commentary Invited Commentary: Positivity in Practice

Positivity, or the experimental treatment assignment assumption, requires that there be both exposed and unexposed participants at every combination of the values of the observed confounders in the population under study. Positivity is essential for inference but is often overlooked in practice by epidemiologists. This issue of the Journal includes 2 articles featuring discussions related to positivity. Here the authors define positivity, distinguish between deterministic and random positivity, and discuss the 2 relevant papers in this issue. In addition, the commentators illustrate positivity in simple 2 3 2 tables, as well as detail some ways in which epidemiologists may examine their data for nonpositivity and deal with violations of positivity in practice. causality; causal inference; confounding factors (epidemiology); epidemiologic methods; exchangeability; identifi-ability; multilevel analysis; propensity score Positivity (1, 2), or the experimental treatment assign-ment assumption (3), is a necessary assumption for causal inference in observational data, along with consistency (4), exchangeability (i.e., no unmeasured confounding and no selection bias), no measurement error, no interference, and correct model specification. Positivity requires that there b