Most strategies for causal inference based on quasi-experimental or observational data critically rely on the assumption of unconfoundedness. If this assumption is suspect, sensitivity analysis can be a viable tool to evaluate the impact of confounding on the analysis of interest. One of the...

Most strategies for causal inference based on quasi-experimental or observational data critically rely on the assumption of unconfoundedness. If this assumption is suspect, sensitivity analysis can be a viable tool to evaluate the impact of confounding on the analysis of interest. One of the...