HAS presents - Some Thoughts on Causal Inference with Observational Data

When:  Sep 23, 2022 from 01:00 to 02:00 (ET)
Most papers in empirical healthcare operations are based on observational data. The limitations in terms of making causal inferences are well known and researchers will typically resort to one of a handful of “identification strategies” popularised in the econometrics literature. These methods are based on untestable assumptions and referees will often question their validity. Editors then end up having to make a judgement call whether or not there is reason to believe that the assumptions are valid beyond reasonable doubt. That’s an unsatisfactory state of affairs. Is there an alternative to this “single model” evidence production? I will argue that it can be more informative if researchers present a well-documented journey through different model specifications, including some traditional causal models. This allows them to shed light on the data from different angles and enables readers to form a more robust judgement of the validity of the estimated effect directions and sizes. I will use a recent paper to exemplify this approach.