Trying to Cure Endogeneity
When prevention strategies such as rich controls, fixed effects, latent controls, temporal separation, and measurement error correction have been implemented and sensitivity analysis still suggests a credible endogeneity threat, researchers can attempt to correct the remaining bias. These methods replace one set of untestable assumptions with another, so they should be treated as a last resort, not a first instinct.
No Free Lunch
IV and copula-based approaches each trade one untestable assumption for another. IV approaches replace the assumption that the predictor is exogenous with the exclusion restriction (the instrument has no direct effect on the outcome). Copula correction replaces the same assumption with distributional and dependence assumptions about unobservables.
Neither approach eliminates the fundamental challenge: Both require careful conceptual justification, and neither can guarantee that endogeneity has been fully resolved.
Descriptive Positioning
When neither credible identification nor triangulation with experiments is possible, researchers should consider pivoting to carefully framed descriptive questions that avoid causal claims altogether. Well-executed descriptive work contributes by establishing empirical regularities that subsequent causal research must explain. This is especially valuable in rapidly evolving domains, such as digital markets, platform economies, AI-mediated consumer interactions where documenting what is happening must precede understanding why.