Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.

ABSTRACT: To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than th

SUBMITTER: Zawadzki RS 

PROVIDER: S-EPMC10201752 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature