Propensity scores are potentially useful tools in observational research. They are distinct from traditional covariate adjustment in that they attempt to disentangle confounders from exposure allocation rather than from outcome effects. Although they can provide value by reducing overfitting risk and loaning nuance to statistical modeling in select situations, propensity scores are generally not any more reliable than traditional adjustment. Further, propensity scores, and particularly PSM, obligate additional considerations that are less important for traditional covariate adjustments. Clinicians and researchers who read or perform propensity score studies should remember, propensity cannot account for unobserved bias or endogeneity, always involve lost information, are susceptible to missing data, and always involve trade-offs between internal and external validity. These issues are particularly relevant to critical care clinical research, and practitioners should appreciate these considerations when interpreting evidence derived from propensity scores.