What is the difference between matching and propensity score matching?

Propensity scores, a particular type of matching methodology, have been found to have multiple beneficial applications beyond their initial purpose. These scores are especially useful in areas such as data weighting and doubly robust estimation.

One notable aspect of propensity scores is their ability to help control for bias in observational studies. By balancing the covariate distributions between treatment and control groups, propensity scores can mitigate the potential for confounding variables to influence the study outcomes.

When employed in conjunction with other matching procedures, such as exact or caliper matching, propensity scores can enhance the overall effectiveness of the matching process. This, in turn, can lead to more reliable and accurate estimates of treatment effects.

Propensity score matching (PSM) is a particularly powerful method for estimating the average treatment effect in observational studies. By utilizing a series of covariates to calculate propensity scores, PSM enables researchers to compare groups of individuals who are similar in all respects except for the treatment they received.

The utilization of matching methods in statistical analysis remains a valuable tool, albeit one that should be combined with other matching strategies for comprehensive results.