How do you address selection bias in causal inference studies?

Propensity Score Matching is a statistical technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM involves the calculation of the propensity score, which is the probability of treatment assignment conditional on observed baseline characteristics. The beauty of this method lies in its simplicity and effectiveness. By matching units with similar propensity scores from the treatment and control groups, we can form a balanced dataset that simulates a randomized experiment, thus reducing selection bias.

To calculate the propensity score, one might use logistic regression, where the treatment assignment is the dependent variable, and the observed characteristics are the independent...