Bayesian probability

Identification (biology)

Internal medicine

Radiology

Statistical inference

Pathology

Mathematical economics

Propensity score matching

Observational study

Inference

Inverse probability weighting

Population

Mathematics

Posterior probability

Biology

Statistics

Machine learning

Outcome (game theory)

Econometrics

Artificial intelligence

Causal inference

Matching (statistics)

Weighting

Marginal structural model

Computer science

Botany

Inverse probability

Medicine

Environmental health

Journal of Epidemiology

Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (ie, matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated.

Using Propensity Scores for Causal Inference: Pitfalls and Tips