Greetings,

I'm running Stata 15.1 on a Mac OS and working with experimental data. I've been conducting tests of mediation with the 'sureg' command (why sureg? because I have a series of pre-treatment covariates, some of which are categorical, and sureg allows the use of prefix 'i' operators) to determine whether or to what extent the effects of an experimental treatment (a dummy variable) on a continuous outcome variable are conveyed via a 3rd continuous post-treatment variable. I'd like to run a sensitivity analysis to test how robust the indirect effects are to violations of the 'no unmeasured confounding of the M-Y relationship' assumption. Unfortunately, I'm not sure if this is possible after a 'sureg'. Does anyone know if it is or how I can go about it? If not possible with sureg, what are my options as far as sensitivity analysis goes?

If it helps, here is the code/program I've been using to run the sureg models + calculate bootstrap standard errors:

Code:
capture program drop bootbm
program bootbm, rclass
  syntax [if] [in]
  sureg (mediatior treatment i.ideo7 i.party7 male age i.educ i.region4)  (outcome treatment mediator i.ideo7 i.party7 male age i.educ i.region4)  `if' `in'
  return scalar indirecteffect  = [mediator]_b[treatment]*[outcome]_b[mediator]
  return scalar totaleffect= [outcome]_b[treatment]+[mediator]_b[treatment]*[outcome]_b[mediator]
  return scalar directeffect=[outcome]_b[treatment]
end
bootstrap r(indirecteffect)  r(totaleffect) r(directeffect),  reps(10000): bootbm
Here is also some sample data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(outcome mediator) double treatment float age long ideo7 float party7 long(educ male region4)
 1.0860398  .57275814 1 73 2 1 4 1 2
  .4070499  .23108044 0 38 6 6 3 0 2
 1.5952822  1.2561136 0 37 1 1 3 0 3
 1.0860398  1.2561136 0 33 1 1 3 0 1
-1.1206771 -1.1356306 0 57 4 5 4 0 4
 1.4255346    .914436 0 64 1 1 4 0 3
-1.2904246  .23108044 1 46 5 6 3 0 3
 1.5952822  .06024148 0 78 2 1 4 1 3
 1.4255346  .57275814 1 31 1 2 3 0 2
 -1.460172   .4019194 1 31 3 2 1 0 4
 -.6114347  -1.818986 0 40 2 2 3 1 1
-.27193987  .57275814 1 49 2 1 2 1 2
 1.4255346  1.2561136 0 29 1 1 4 0 1
 -1.460172   -.452275 0 25 1 1 3 1 4
   .746545  .57275814 1 41 3 1 4 1 4
  .2373026  .23108044 1 37 1 1 3 1 3
 1.2557874  1.2561136 1 42 2 1 3 1 3
  .4070499  .06024148 1 31 1 1 1 1 2
 1.2557874    .914436 0 32 1 1 3 0 4
 1.0860398  1.2561136 0 69 2 1 3 0 3
 1.2557874  1.2561136 0 26 1 1 3 1 3
 1.5952822  1.2561136 0 51 1 1 3 0 3
-1.1206771 -1.1356306 1 68 6 6 2 0 2
  .5767974  .06024148 0 33 1 2 2 0 2
 1.5952822  1.2561136 1 27 1 1 2 0 4
-.10219235   -.452275 1 38 2 1 2 0 3
  .2373026  .57275814 0 50 2 1 4 0 3
   .746545  1.2561136 1 70 3 3 1 0 3
  .4070499   .7435971 0 35 2 3 3 1 1
 -1.460172 -1.4773084 0 38 7 7 1 0 3
 1.4255346  1.2561136 0 36 1 3 2 1 4
 1.4255346  1.2561136 1 67 2 1 4 1 2
 -.9509296 -1.1356306 0 47 6 6 3 1 4
 1.0860398  1.2561136 1 45 2 1 2 0 2
-.27193987 -.11059724 1 53 6 7 2 0 4
 1.2557874  1.2561136 0 37 2 2 1 1 3
  .5767974  1.0852747 1 28 3 1 3 0 2
 1.2557874 -.11059724 1 27 2 1 2 1 4
-1.2904246 -.11059724 0 64 1 1 3 1 1
 -1.460172 -1.3064694 1 34 5 6 2 0 4
 .06755506  .57275814 1 54 2 1 3 0 3
 .06755506    .914436 0 44 3 3 2 1 4
 -.4416873  .23108044 0 28 5 6 3 0 2
 -.9509296  -.2814362 0 39 6 7 3 1 4
 1.0860398  1.0852747 0 26 1 1 1 0 2
  .5767974   .7435971 0 28 2 2 2 1 1
 1.0860398  1.0852747 0 25 1 1 3 0 3
-.27193987  .23108044 0 42 1 1 2 1 3
 1.0860398  1.2561136 0 33 2 2 4 0 2
  .5767974  .23108044 1 56 3 3 4 1 1
 -.4416873  -.9647917 0 22 3 1 3 0 1
 .06755506   .7435971 0 49 2 3 2 1 4
 1.2557874  1.0852747 1 23 1 1 2 0 3
  .9162923    .914436 1 22 5 6 3 1 2
 1.0860398   .7435971 1 36 1 1 3 1 2
 -1.460172  -1.818986 1 30 6 7 3 0 1
 -.9509296  -.7939528 0 23 2 2 4 1 1
   .746545  1.2561136 1 60 3 1 1 0 2
 1.4255346  1.0852747 0 41 2 2 2 1 3
 -.6114347  -.7939528 1 36 3 1 2 0 3
 1.4255346  .57275814 1 39 1 1 3 1 1
 -.4416873 -.11059724 1 30 3 4 2 0 4
  .5767974    .914436 0 26 3 2 4 1 1
 -1.460172  -.7939528 1 60 3 4 3 1 3
 1.2557874   .7435971 1 33 1 1 3 1 1
  .5767974  1.2561136 0 24 1 3 1 1 2
  .5767974  .06024148 1 57 2 1 4 0 2
  .9162923    .914436 1 38 3 2 4 1 1
  .9162923   .7435971 0 31 4 3 4 0 4
 1.5952822  1.2561136 1 61 2 1 2 0 3
 -1.460172  -1.818986 1 40 6 7 4 1 2
 -1.460172  -.9647917 1 28 6 6 2 0 2
-.10219235 -1.3064694 1 39 2 2 4 0 3
  .9162923   .4019194 1 69 1 1 2 0 3
 -.4416873  .57275814 1 46 3 2 2 0 3
  .9162923  1.2561136 0 33 1 1 3 0 1
 .06755506  .23108044 0 35 4 2 4 1 3
 -.7811822  .06024148 1 49 3 2 3 0 4
 -.4416873  1.2561136 0 36 2 3 3 1 3
 .06755506  .57275814 1 34 2 1 3 0 1
 -.4416873  -.7939528 1 42 5 6 3 1 4
-.27193987  1.0852747 0 48 3 6 3 0 3
-.10219235  1.2561136 0 24 1 1 4 0 3
 -.4416873  -.2814362 1 52 6 5 4 1 3
   .746545  1.0852747 1 32 1 1 4 0 2
 1.4255346  1.2561136 1 36 1 3 2 0 4
 1.5952822   .7435971 0 33 2 2 2 1 4
 -1.460172  -1.818986 0 40 7 7 3 0 2
-.27193987  .57275814 0 38 3 2 3 1 3
  .5767974  1.2561136 1 24 3 2 2 0 3
  .9162923  -.2814362 1 46 2 1 4 0 3
 1.5952822  1.2561136 1 40 1 3 4 1 2
-.10219235  -.2814362 1 25 2 1 3 0 2
  .2373026   -.452275 0 23 4 5 1 0 2
  .5767974  .57275814 0 33 1 3 4 0 3
-.10219235  .23108044 0 29 1 1 3 1 3
 1.0860398  .57275814 1 40 2 2 4 1 4
 1.5952822  1.2561136 0 26 1 1 3 1 2
 .06755506  .57275814 0 24 2 1 3 0 4
 -.9509296   -.452275 0 35 3 2 2 0 3
end
label values ideo7 ideo7
label def ideo7 1 "Conservative", modify
label def ideo7 2 "Liberal", modify
label def ideo7 3 "Moderate/Middle of the road", modify
label def ideo7 4 "Slightly conservative", modify
label def ideo7 5 "Slightly liberal", modify
label def ideo7 6 "Very conservative", modify
label def ideo7 7 "Very liberal", modify
label values educ educ
label def educ 1 "4 year/Bachelor’s degree", modify
label def educ 2 "Doctoral or Professional degree", modify
label def educ 3 "High school graduate", modify
label def educ 4 "Less than high school graduate", modify
Thanks in advance for any help you can provide!