What are the challenges in using observational data for causal inference in healthcare?

First, let's address the issue of confounding variables. These are variables that can influence both the treatment and the outcome, potentially leading to erroneous causal interpretations. To mitigate this, I've had success using propensity score matching. This method involves matching units (such as patients) receiving the treatment with similar units not receiving the treatment on the basis of a set of observed characteristics. This approach helps to mimic some of the conditions of a randomized experiment by ensuring that the comparison groups are as similar as possible, reducing the bias introduced by confounders.

Another significant challenge is selection bias. This occurs when the subjects included in the study are not representative of the broader population, which can lead to skewed results. A technique...