Introduction to IV

The goal in much of medical research is to answer the question: “What will a patient’s outcome, Y, be if the patient is given a specific treatment, T?” We focus our efforts on understanding how to intervene in order to improve outcomes for our patients. Some of the strategies for estimating the effect of a treatment are well known. The foremost technique is the foundational technique of Science: the controlled and randomized experiment. A well run experiment’s conclusions are highly regarded and often interpretable as establishing causal links between treatments and outcomes. But the ability to run a clinical trial – with its heavy demands on resources and time as well as ethical considerations – limits the situations in which we can construct an experiment. It would be beneficial if we could use data generated by the world, referred to as “observational data,” to answer some of the questions about causal links between treatment and control. Examples of observational datasets are: insurance billing data, medical records and administrative data from hospital records. These datasets are in contrast to those generated by clinical trials.