Propensity score matching (PSM)效能检测的方法effectiveness evaluation
发表于 ： 2023年 3月 5日 18:17
To evaluate the effectiveness of PSM, several methods can be used:
Balance checking: This involves checking the balance of covariates between the treatment and control groups before and after matching. The goal is to ensure that the distribution of covariates is similar between the two groups, which indicates that confounding has been reduced. Balance can be assessed using standardized mean differences or other statistical tests.
Overlap checking: This involves checking the overlap of propensity scores between the treatment and control groups. The goal is to ensure that there is sufficient overlap, which means that the groups are comparable and the matching is appropriate.
Sensitivity analysis: This involves assessing the sensitivity of the results to unobserved confounding. This can be done by varying the specification of the propensity score model or including additional covariates in the model.
Effect size estimation: This involves estimating the effect size of the treatment on the outcome before and after matching. The goal is to determine if the effect size has changed after matching, which indicates that confounding has been reduced.
Overall, the effectiveness of PSM depends on the quality of the propensity score model and the degree of overlap between the treatment and control groups. It is important to assess the quality of matching using the methods above and to interpret the results in light of any limitations or assumptions of the PSM method.
Balance checking: This involves checking the balance of covariates between the treatment and control groups before and after matching. The goal is to ensure that the distribution of covariates is similar between the two groups, which indicates that confounding has been reduced. Balance can be assessed using standardized mean differences or other statistical tests.
Overlap checking: This involves checking the overlap of propensity scores between the treatment and control groups. The goal is to ensure that there is sufficient overlap, which means that the groups are comparable and the matching is appropriate.
Sensitivity analysis: This involves assessing the sensitivity of the results to unobserved confounding. This can be done by varying the specification of the propensity score model or including additional covariates in the model.
Effect size estimation: This involves estimating the effect size of the treatment on the outcome before and after matching. The goal is to determine if the effect size has changed after matching, which indicates that confounding has been reduced.
Overall, the effectiveness of PSM depends on the quality of the propensity score model and the degree of overlap between the treatment and control groups. It is important to assess the quality of matching using the methods above and to interpret the results in light of any limitations or assumptions of the PSM method.