How do you address selection bias in causal inference studies?

Thank you for posing such an insightful question. Addressing selection bias is crucial in causal inference studies, particularly within observational settings, where the risk of encountering biases is inherently high due to the lack of randomized control. As a Data Scientist, my approach to mitigating selection bias involves several strategic steps, but one method I've found particularly effective is the use of Propensity Score Matching (PSM). 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...