Empirically assessing the plausibility of unconfoundedness (Dr. Fernando Hartwig)

In this video, Dr. Fernando Pires Hartwig describes a simple procedure for assessing the plausibility that a pre-selected set of covariates is sufficient for eliminating confounding by statistical adjustment.
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 lecture, a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate set of covariates suffices for confounding adjustment) is presented. This approach 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. The presentation focuses on the assumptions underlying the method, which is illustrated 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.
This seminar was presented at the MRC IEU (University of Bristol, UK) on January 15, 2024.
Useful links:
- Dr. Hartwig's CV: lattes.cnpq.br/....
- Preprint of the paper: arxiv.org/abs/...