{Reference Type}: Journal Article
{Title}: Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects.
{Author}: Mathur MB;Shpitser I;
{Journal}: Am J Epidemiol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 20
{Factor}: 5.363
{DOI}: 10.1093/aje/kwae145
{Abstract}: When analyzing a selected sample from a general population, selection bias can arise relative to the causal average treatment effect (ATE) for the general population, and also relative to the ATE for the selected sample itself. We provide simple graphical rules that indicate: (1) if a selected-sample analysis will be unbiased for each ATE; (2) whether adjusting for certain covariates could eliminate selection bias. The rules can easily be checked in a standard single-world intervention graph. When the treatment could affect selection, a third estimand of potential scientific interest is the "net treatment difference", namely the net change in outcomes that would occur for the selected sample if all members of the general population were treated versus not treated, including any effects of the treatment on which individuals are in the selected sample . We provide graphical rules for this estimand as well. We decompose bias in a selected-sample analysis relative to the general-population ATE into: (1) "internal bias" relative to the net treatment difference; (2) "net-external bias", a discrepancy between the net treatment difference and the general-population ATE. Each bias can be assessed unambiguously via a distinct graphical rule, providing new conceptual insight into the mechanisms by which certain causal structures produce selection bias.