Propensity Score Weighting Analysis

Unlike randomized clinical trials, observational studies must adjust for differences such as confounding to ensure patient characteristics are comparable across treatment groups. This is frequently addressed through propensity scores (PS), which summarizes differences in patient characteristics between treatment groups. Propensity Score is the probability that each individual will be assigned to receive the treatment of interest given their measured covariates. Matching or Weighting on the PS is used to adjust comparisons between the 2 groups, thus reducing the potential bias in estimated effects of observational studies.

The following use cases assume a binary treatment or exposure in order to infer causality. Given a treatment and control with one outcome observed per unit, can we estimate the treatment effect? Note we can only estimate the treatment effect, identification of causality is not possible through observational studies.

Estimation of Propensity Scores

Propensity scores are most commonly estimated

References

Overlap Weighting: A Propensity Score Method That Mimics Attributes of a Randomized Clinical Trials (Thomas, Li, Pencina; 2020)