Asking for help/refs: I have observational data where some patients always get treatment T=A due to their diagnosis (D) while the rest get either T=A or T=B based on clinical judgement. It seems to me that it might be possible to get closer to causal claims for T by exploiting the (lack of) differences between the "T=A, D=1" and "T=A, D=0" groups. This seems akin to IV analysis with D as instrument. Any ideas? A bit more detail at: https://discourse.datamethods.org/t/observational-data-with-diagnosis-partially-determining-treatment-imperfect-instrumental-variable/6983 #stats #instrumentalVariable #biostats
Observational data with diagnosis partially determining treatment (imperfect instrumental variable?)

Here’s the setting - we have a retrospective dataset of patients undergoing a certain surgery. The surgery has two variants, let’s call them A and B. We are interested in comparing a set of outcomes like length of hospital stay and blood loss in each variant. The choice of variant is based on clinical judgement and thus there are inevitable selection effects biasing the comparison. If that was all, we’d have little options beyond just reporting the results descriptively. We however have an extr...

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