Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

ABSTRACT: Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approach

SUBMITTER: Matthay EC 

PROVIDER: S-EPMC6926350 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature