Dear statalisters,

Based on the paper of King and Nielson (2016) on the comparative analysis of matching methods, I decided to not 'blindly' decide on using propensity scores for matching but also applied other algorithms (Mahalanobis and Exact) to analyse the most efficient one. For this aim, I am working with the kmatch command and have tried a few things:

Code:
Kernel matching:
 kmatch md round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nate
kmatch em round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nate
kmatch ps round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nn

Nearest neighbour: 1:1 and 1:5
kmatch ps round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nn
kmatch md round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nn
kmatch md round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nn(5)
kmatch ps round age_1 schoolatt childrenattendschool partner howmanyHHM_1   ( pcaenvironmental1 pcaenvironmental2 ), att nn(5)
However, whereas for propensity score matching under psmatch2 it is quite clear how to analyse the common support (in a nice, and visual manner) I have not really found out how to clearly observe the best method. The outputs I get are e.g. for Kernel matching under kmatch:

Mahalanobis
Array

Propensity Score

Array

Exact matching

Array

Based on this, I would assume that the latter (exact matching) is the best option as the standard deviation is the lowest as well as the variance ratio closest to 1? However, this method only used 41 out of the 150 treated cases..

I am a bit confused, would be great if someone would have some advise!

Linda