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

Thank you for posing such a crucial and thought-provoking question. Causal inference in healthcare is indeed a complex challenge, primarily because observational data, unlike randomized control trials, are not collected under controlled experimental conditions. This often leads to issues such as confounding variables, selection bias, and reverse causation which can obscure true causal relationships. However, my experience as a Data Scientist, specifically working on healthcare-related projects, has equipped me with a robust toolkit for navigating these challenges. 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...