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Figures 2 and 3 show the distribution of propensity scores across treatment and control groups.
Fig. 1 The distribution of propensity scores for treated and untreated groups.
Figure 5 shows the distribution of propensity scores of matched and unmatched individuals in both groups.
Figure 1 shows the density estimates of the distribution of propensity scores for each group, along with the areas with and without common support.
The distribution of propensity scores between outpatient and inpatient cases was more similar after matching, suggesting that these cases are similar in terms of individual patient characteristics and demographics.
The distribution of propensity scores in OAI is right skewed and concentrated in the 0.0 to 0.2 range, while the distribution is fairly uniform across all values in NHANES-III, indicating substantial differences in populations participating in each study.
Similar(50)
The distributions of propensity scores in wood and NG users differed but provided sufficient range of propensity scores in both groups to control for them in multivariate modeling.
The adequacy of the propensity model was assessed by checking the distribution of the propensity scores by treatment for a reasonable overlap and the pre- and post-inverse probability of treatment-weighting balance of the covariates [ 23].
The result guarantees a sufficient overlap in the distribution of the propensity score between adopters and non-adopters.
Figure 2 in Appendix presents the distribution of predicted propensity scores of the migrants and non-migrants.
Another issue of concern with matching and reweighting estimators of treatment effects is the degree of overlap in the distribution of the propensity score.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com