Matching is a statistical techniques used in research to create comparable groups or pairs of subjects based on certain characteristics. The primary goal of matching is to reduce bias and control for potential confounding variables in observational studies, where participants are not randomly assigned to different conditions.

There are several methods of matching, and one common approach is to match participants in the treatment group with those in the control group who have similar or identical characteristics. The matching process typically involves selecting covariates (variables that may influence both the treatment assignment and the outcome) and ensuring that the distribution of these covariates is similar between the treatment and control groups.

Matching can be done using various algorithms, such as exact matching, propensity score matching, or nearest-neighbor matching. The choice of method depends on the study design and the nature of the data.

By creating comparable groups through matching, researchers aim to make the groups more similar on observed characteristics, thus improving the internal validity of the study and enhancing the ability to draw more accurate conclusions about the causal relationship between the treatment and the outcome of interest.