, Volume 12, Issue 4, pp 235-236
Date: 23 Nov 2012

Preface to HSOR special issue on instrumental variable methods

This is an excerpt from the content

Selection bias is a central issue for health services and outcomes research. In observational studies, individuals who receive a treatment often differ from those who do not. Even in randomized controlled trials, those who comply with the protocol often differ from those who do not. Within the field of causal inference, there are methods for adjusting for measured confounders; however, remaining bias due to unmeasured confounders and model misspecification is always a concern. About 10 years ago, Health Services & Outcomes Research Methodology published a special issue on Causal Inference that was well-received in the field (Ash et al. 2001). The current special issue builds upon the previous special issue, with a focus on Instrumental Variable (IV) method, an approach for making causal inference about the effect of a treatment, even when there is unmeasured confounding. This is particularly attractive, as other causal methods such as propensity score matching can adjust only for obser ...