  The possibility of unmeasured confounding is one of the main limitations for
causal inference from observational studies. There are different methods for
partially empirically assessing the plausibility of unconfoundedness. However,
most currently available methods require (at least partial) assumptions about
the confounding structure, which may be difficult to know in practice. In this
paper we describe a simple strategy for empirically assessing the plausibility
of conditional unconfoundedness (i.e., whether the candidate set of covariates
suffices for confounding adjustment) which does not require any assumptions
about the confounding structure, requiring instead assumptions related to
temporal ordering between covariates, exposure and outcome (which can be
guaranteed by design), measurement error and selection into the study. The
proposed method essentially relies on testing the association between a subset
of covariates (those associated with the exposure given all other covariates)
and the outcome conditional on the remaining covariates and the exposure. We
describe the assumptions underlying the method, provide proofs, use simulations
to corroborate the theory and illustrate the method with an applied example
assessing the causal effect of length-for-age measured in childhood and
intelligence quotient measured in adulthood using data from the 1982 Pelotas
(Brazil) birth cohort. We also discuss the implications of measurement error
and some important limitations.
