Hi everyone,

I am still figuring out PSM for my study.I am having trouble in the first step matching. When i graph the histogram for before and after matching there is not much of a difference in the two. I am not sure if it is because the confounding variables i have selected are weak or it is because there is something wrong in the way my codes are formed. Could it be that the dataset i have the treated and control groups are already balanced? but this is a cross sectional data not RCT. I am desperately looking for some help and clarity so i can move on to evaluating ATT. Thank you so much for all the help.

in STEP 1: i use the probit
Code:
probit $TREAT $HH $GEO $HH_RESP
predict pre_probit, p
in Step 2 : i use psmatch2

Code:
psmatch2 $TREAT $HH $GEO $HH_RESP
sum _pscore
sum _pscore if $TREAT ==1
sum _pscore if $TREAT ==0
psgraph

in Step 3: i make a two way histogram before matching

Code:
**Before matching overlaid Histograms
twoway (histogram _pscore if $TREAT==1, blcolor("255 0 0") bfcolor(none)) (histogram _pscore if $TREAT==0, blcolor(teal) bfcolor(none))
then finally in Step 4: I make a two way histogram after matching

Code:
*** histogram: viusal analysis
//***Matching Quality***//
sum _pscore & _weight~=.
sum _pscore if $TREAT ==1 & _weight~=.
sum _pscore if $TREAT ==0 & _weight~=.

histogram _pscore if $TREAT==1 & _weight~=.,frequency
histogram _pscore if $TREAT==0 & _weight~=.,frequency

*psm
twoway (histogram _pscore if $TREAT == 0, blcolor("red") bfcolor(none) legend(label(1 "M"))) (histogram _pscore if $TREAT == 1, blcolor(teal) bfcolor(none) legend(label(2 "N"))), name(PSM_grp_1)

twoway (histogram _pscore if $TREAT  == 0 & _weight ~=., frequency fcolor(none) lcolor(red) lpattern(solid) legend(label(1 "M"))) (histogram _pscore if $TREAT  == 1 & _weight ~=., frequency fcolor(none) lcolor(teal) lpattern(solid) legend(label(2 "N"))), name (PSM_grp_2)
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float migrant byte language float dep_ratio byte(ind_hou shrd_hou temp_hou district jamoat) int village byte(HHH rel_spouse married unmarried widowed divorced) float(S1C_age age2)
0 1 .11111111 1 0 0 1  1 19 0 1 1 0 0 0 46 2116
0 1 .16666667 1 0 0 6 23 63 0 1 1 0 0 0 37 1369
1 1 .22222222 1 0 0 1  1 19 0 1 1 0 0 0 30  900
1 1 .16666667 0 1 0 1  1 19 0 0 1 0 0 0 46 2116
0 1 .44444445 1 0 0 6 23 63 0 1 1 0 0 0 55 3025
1 1 .11111111 0 1 0 1  1 19 0 1 1 0 0 0 60 3600
1 1 .22222222 1 0 0 6 23 63 0 0 1 0 0 0 31  961
1 1 .22222222 1 0 0 1  1 19 0 1 1 0 0 0 35 1225
1 1 .16666667 1 0 0 6 23 63 0 1 1 0 0 0 51 2601
1 1 .22222222 1 0 0 6 22 62 0 1 1 0 0 0 38 1444
1 1 .16666667 0 1 0 1  1 19 1 0 0 0 1 0 59 3481
1 1  .3333333 1 0 0 6 22 62 0 1 1 0 0 0 45 2025
1 1  .2777778 1 0 0 1  1 19 0 1 1 0 0 0 29  841
0 1  .2777778 0 1 0 1  1 19 0 1 1 0 0 0 66 4356
0 1  .2777778 1 0 0 6 22 62 0 0 1 0 0 0 24  576
1 0 .16666667 1 0 0 6 22 62 0 1 1 0 0 0 50 2500
1 1 .16666667 1 0 0 6 22 61 0 1 1 0 0 0 29  841
1 1 .22222222 0 1 0 1  1 19 0 0 1 0 0 0 50 2500
1 1  .3333333 1 0 0 6 22 61 0 0 1 0 0 0 25  625
0 1 .44444445 0 1 0 1  1 19 0 0 0 0 1 0 54 2916
1 1 .16666667 1 0 0 6 22 61 0 1 1 0 0 0 59 3481
1 1  .2777778 0 1 0 1  1 19 0 0 0 0 1 0 65 4225
0 1 .05555556 1 0 0 6 22 61 0 1 1 0 0 0 56 3136
0 1  .2777778 1 0 0 1  1 19 0 0 1 0 0 0 36 1296
0 1 .11111111 1 0 0 6 21 60 0 1 1 0 0 0 64 4096
0 1 .22222222 1 0 0 6 21 60 0 1 1 0 0 0 59 3481
0 1  .3888889 1 0 0 6 22 57 0 0 1 0 0 0 64 4096
1 1 .44444445 1 0 0 6 20 57 1 0 0 0 1 0 64 4096
1 1 .44444445 1 0 0 6 20 57 0 0 1 0 0 0 33 1089
1 1  .2777778 1 0 0 6 20 57 0 1 1 0 0 0 54 2916
0 1         0 1 0 0 6 18 54 0 1 1 0 0 0 76 5776
0 1 .11111111 1 0 0 6 18 54 0 1 1 0 0 0 44 1936
0 1 .05555556 1 0 0 6 18 54 0 0 0 1 0 0 27  729
1 1 .22222222 1 0 0 6 18 54 0 0 1 0 0 0 26  676
1 1 .05555556 1 0 0 1  1 20 0 1 1 0 0 0 46 2116
0 1 .11111111 1 0 0 6 18 54 0 1 1 0 0 0 48 2304
0 1 .16666667 1 0 0 1  1 20 0 1 1 0 0 0 50 2500
1 1 .11111111 1 0 0 6 18 54 1 0 0 0 1 0 63 3969
0 1 .22222222 0 1 0 1  1 20 0 0 0 0 1 0 52 2704
1 1 .16666667 1 0 0 6 21 60 0 0 0 0 1 0 35 1225
1 1 .16666667 1 0 0 1  1 20 0 0 0 0 1 0 31  961
0 1  .2777778 1 0 0 6 21 60 0 1 1 0 0 0 42 1764
1 1  .2777778 0 1 0 1  1 20 0 0 1 0 0 0 43 1849
1 1  .6111111 1 0 0 6 18 53 1 0 0 0 1 0 51 2601
1 1  .3333333 1 0 0 6 18 53 0 1 1 0 0 0 47 2209
1 1  .6666667 0 1 0 1  1 20 0 0 1 0 0 0 35 1225
1 1 .16666667 0 1 0 1  1 20 1 0 0 0 1 0 50 2500
0 1  .2777778 1 0 0 6 18 53 0 1 1 0 0 0 41 1681
1 1 1.0555556 0 1 0 1  1 20 0 0 1 0 0 0 37 1369
1 1 .11111111 1 0 0 6 18 53 0 1 1 0 0 0 37 1369
0 1  .3333333 0 1 0 1  1 20 0 0 1 0 0 0 33 1089
1 1 .22222222 1 0 0 6 18 53 0 1 1 0 0 0 35 1225
0 1         0 1 0 0 1  1 20 0 0 1 0 0 0 49 2401
0 1         0 1 0 0 6 21 60 0 1 1 0 0 0 54 2916
0 1 .11111111 1 0 0 1  1 20 0 1 1 0 0 0 39 1521
0 1 .44444445 1 0 0 6 18 53 0 1 1 0 0 0 34 1156
0 1  .3333333 1 0 0 6 23 64 0 0 1 0 0 0 26  676
1 1  .6111111 0 1 0 1  1 20 0 1 1 0 0 0 57 3249
1 1  .3333333 1 0 0 6 23 64 0 0 1 0 0 0 31  961
0 1 .11111111 1 0 0 6 23 64 0 1 1 0 0 0 48 2304
0 1  .3333333 1 0 0 6 23 64 0 1 1 0 0 0 41 1681
1 1  .3333333 1 0 0 6 23 64 0 1 1 0 0 0 67 4489
1 1 .11111111 1 0 0 6 23 64 0 1 1 0 0 0 55 3025
1 0 .22222222 1 0 0 6 21 59 0 1 1 0 0 0 33 1089
1 1 .22222222 0 1 0 6 19 56 0 1 1 0 0 0 45 2025
0 1 .22222222 1 0 0 6 19 56 0 1 1 0 0 0 31  961
0 1  .2777778 1 0 0 6 19 55 0 1 1 0 0 0 62 3844
1 1  .2777778 1 0 0 6 19 55 0 0 0 0 1 0 56 3136
1 1 .11111111 1 0 0 6 19 55 0 0 1 0 0 0 22  484
1 1         0 0 1 0 1  6 21 0 1 1 0 0 0 50 2500
1 1 .11111111 1 0 0 6 19 55 0 0 1 0 0 0 23  529
1 1 .11111111 1 0 0 1  6 21 0 1 1 0 0 0 50 2500
1 1 .16666667 1 0 0 6 19 55 0 1 1 0 0 0 47 2209
1 1  .2777778 1 0 0 1  6 21 0 0 1 0 0 0 67 4489
0 1 .16666667 1 0 0 6 19 55 0 1 1 0 0 0 41 1681
1 1 .16666667 1 0 0 6 21 59 0 1 1 0 0 0 37 1369
1 1  .3888889 0 1 0 1  6 21 0 1 1 0 0 0 61 3721
1 1 .16666667 1 0 0 6 21 59 0 1 1 0 0 0 25  625
1 1  .8333333 0 1 0 1  6 21 0 0 0 0 1 0 32 1024
1 1  .3333333 1 0 0 6 21 59 1 0 0 0 1 0 53 2809
0 1 .16666667 1 0 0 1  6 21 0 0 0 0 1 0 63 3969
1 1 .11111111 1 0 0 6 19 56 0 1 1 0 0 0 28  784
0 1 .22222222 1 0 0 6 23 63 0 1 1 0 0 0 38 1444
0 1 .16666667 1 0 0 1  6 21 0 1 1 0 0 0 36 1296
0 1 .22222222 0 1 0 1  6 21 0 0 1 0 0 0 26  676
1 1 .22222222 1 0 0 1  6 21 0 0 1 0 0 0 26  676
1 1  .2777778 1 0 0 1  6 21 0 1 1 0 0 0 40 1600
1 1 .05555556 1 0 0 1  6 21 0 1 1 0 0 0 44 1936
1 1 .11111111 1 0 0 1  6 21 0 1 1 0 0 0 39 1521
1 1 .11111111 0 1 0 1  6 22 0 1 1 0 0 0 48 2304
1 1 .11111111 0 1 0 1  6 22 0 1 1 0 0 0 42 1764
0 1 .16666667 1 0 0 1  6 22 1 0 1 0 0 0 56 3136
0 1 .05555556 1 0 0 1  6 22 0 1 1 0 0 0 54 2916
0 1 .11111111 1 0 0 1  6 22 0 1 1 0 0 0 45 2025
0 1 .11111111 1 0 0 1  6 22 0 1 1 0 0 0 41 1681
0 1 .11111111 0 1 0 1  6 22 0 1 1 0 0 0 55 3025
1 1  .3888889 0 1 0 1  6 22 0 1 1 0 0 0 50 2500
0 1 .16666667 0 1 0 1  6 22 1 0 0 0 1 0 57 3249
0 1 .22222222 1 0 0 1  6 22 0 1 1 0 0 0 33 1089
0 1 .11111111 0 1 0 1  6 22 0 0 1 0 0 0 25  625
end