## Causal perspective in CER

Preparing a presentation at SPPH, I used Pearl’s counterfactual probabilities in the context of coronary revascularization.

In public health, policy makers are often interested in risk reduction that would be achieved if exposure were reduced to an alternative level. Similarly, policymakers involved in health care are really interested to know whether patients would benefit from an alternative treatment, which is more suitable to clinical profiles of patients.

One immediate question in comparing alternative strategies for coronary revascularization (PCI v. CABG) is What would be the impact on the system if some PCI patients would be treated with CABG? For example, what proportion of repeat revascularizations could be avoided had patients suitable for CABG had it instead of PCI?

Another policy question is What would be the impact on the patient population if all CABG patient were undergoing PCI? For example, what proportion of stroke could be avoided had patients suitable for PCI undergone PCI instead of CABG?

In addition to attributable proportion, known to epidemiologists, Pearl offered other quantities that help answer the variety of policy questions arising from CER.

Probability of disablement refers to hypothesis that the absence of exposure would prevent outcome, when we observe that in fact outcome did occur—proportion of observed cases in the total patient population that would not have occurred had the population been entirely unexposed.

Probability of necessity refers to expectations that the absence of exposure would prevent outcome, when we observe that both exposure and outcome did occur.

If interested in the opposite situation we turn to the other two probabilities. Probability of enablement refers to hypothesis that the presence of exposure would produce outcome, given that in fact outcome did not occur.

Probability of susceptibility refers to expectations that the presence exposure would produce outcome, when we observe that neither exposure nor outcome occurred.

If we go back to coronary revascularization, there are two interesting examples of Pearl’s approach. If interested in attribution, we can estimate the proportion of PCI patients who would have survived had they undergone CABG. That is, we consider patients who underwent PCI and died, and ask how many of deaths would be avoided if they undergone CABG instead.

The second example focuses on patients who underwent CABG. If interested in susceptibility, we can estimate the proportion of CABG patients who would have died had they undergone PCI. That is, we consider patients who were suitable and underwent CABG, and ask what would be risk of death if they undergone PCI instead.

These are just two examples that help understand the types of questions that could be asked. Pearl showed that answering causal questions is not limited to randomized trials. Rather it is a function of our ability to incorporate the existing knowledge of causal pathways into identifying the set of factors for adjustment.