How do you validate the assumptions of a causal inference model?

To begin with, at the core of causal inference models lie several key assumptions, including the independence of treatment assignment (often referred to as the Ignorability or Unconfoundedness assumption), the consistency of treatment effects, and the positivity (or overlap) assumption. Validating these assumptions involves a blend of theoretical reasoning, empirical testing, and robust sensitivity analysis.

Firstly, for the independence of treatment assignment, we need to ensure that the assignment to the treatment or control group is independent of potential outcomes. This is often violated in observational studies due to confounding variables. One method to assess this assumption is by implementing propensity score matching. By matching units with similar propensity scores—probabilities of receiving the treatment based on observed covariates—we can mimic a randomized experimental design. After matching, we can then check the balance of covariates across groups to...