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psweight_example.log
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psweight_example.log
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----------------------------------------------------------------------------------------------------------------------------------------------------------------
name: psweight_example
log: C:\Users\kkranker\Documents\Stata\psweight\code-psweight\psweight_example.log
log type: text
opened on: 5 Nov 2019, 22:15:35
.
. // ***************************************************************************
. *! psweight_example.do
. *! IPW- and CBPS-type propensity score reweighting, with extentions
. *! Examples
. //
. *! By Keith Kranker
. //
. // Copyright (C) Mathematica Policy Research, Inc.
. // This code cannot be copied, distributed or used without the express written
. // permission of Mathematica Policy Research, Inc.
. // ***************************************************************************
.
. about
Stata/MP 16.0 for Windows (64-bit x86-64)
Revision 16 Oct 2019
Copyright 1985-2019 StataCorp LLC
Total physical memory: 32.00 GB
Available physical memory: 24.95 GB
Single-user 64-core Stata network license expires 15 Nov 2019:
Serial number: 501609253383
Licensed to: SCCMAdminRoot
Mathematica
. set type double
. di as txt "Current user: `c(username)'" _n "Environment: `c(os)' `c(machine_type)' `: environment computername'" _n "Stata: `c(stata_version)'" cond(c(stata_v
> ersion)==c(version),""," (set to version `c(version)')") _n "Date: " c(current_date) " " c(current_time)
Current user: KKranker
Environment: Windows PC (64-bit x86-64) KKRANKER
Stata: 16
Date: 5 Nov 2019 22:15:35
.
. which psweight
.\psweight.ado
*! psweight.ado
*! IPW- and CBPS-type propensity score reweighting, with various extentions
*! Stata command to estimate models
*! By Keith Kranker
. mata: mata describe using lpsweight
(library contains 58 members)
# bytes type name and extent
-------------------------------------------------------------------------------
4,552 auto classdef scalar psweight()
852 auto real matrix ::Ct()
1,408 auto void ::balanceresults()
2,276 auto real matrix ::balancetable()
628 auto void ::calcN()
816 auto void ::calccovariances()
320 auto void ::calcmeans()
576 auto void ::calcvariances()
356 auto real rowvector ::cbps()
2,136 auto void ::cbps_port_r()
768 auto void ::cbps_port_stata()
676 auto real matrix ::cbps_port_stata_gradient()
752 auto real colvector ::cbps_port_stata_moments()
1,668 auto real matrix ::cbps_port_stata_wgt_matrix()
3,312 auto void ::cbpseval()
356 auto real rowvector ::cbpsoid()
592 auto void ::clone()
568 auto real rowvector ::diagvariance()
360 auto real rowvector ::diff()
1,804 auto void ::fill_vars()
1,516 auto real matrix ::get_N()
64 auto real colvector ::get_pscore()
64 auto real colvector ::get_weight()
64 auto real colvector ::get_weight_mtch()
736 auto real rowvector ::ipw()
1,900 auto real rowvector ::logitbeta()
524 auto real colvector ::logitpredict()
1,296 auto real colvector ::logitweights()
360 auto real scalar ::max_asd()
360 auto real scalar ::mean_asd()
352 auto real scalar ::mean_sd()
436 auto real scalar ::mean_sd_sq()
96 auto void ::new()
688 auto real rowvector ::olsbeta()
468 auto real colvector ::olspredict()
276 auto real rowvector ::pomean()
756 auto void ::postbeta()
2,000 auto real rowvector ::progdiff()
712 auto void ::reweight()
232 auto real rowvector ::sd_sq()
1,448 auto void ::set()
388 auto void ::set_depvars()
6,560 auto real rowvector ::solve()
1,432 auto void ::st_set()
400 auto void ::st_set_depvars()
1,012 auto real rowvector ::stddiff()
2,068 auto real rowvector ::stdprogdiff()
512 auto real colvector ::trim()
368 auto real rowvector ::varratio()
288 auto real scalar ::wgt_cv()
356 auto real scalar ::wgt_kurtosis()
580 auto real scalar ::wgt_max()
784 auto real rowvector ::wgt_moments()
228 auto real scalar ::wgt_sd()
372 auto real scalar ::wgt_skewness()
412 auto void psweight_cbps_eval()
2,276 auto void psweight_init_mlopts()
504 auto void psweight_logit_eval()
-------------------------------------------------------------------------------
.
. if 1 {
.
. ************************************************************************************
. * Simple examples in the help file (psweight.sthlp)
. ************************************************************************************
.
. // Setup
. webuse cattaneo2
(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154)
.
. // Balance before reweighting
. psweight balanceonly mbsmoke mmarried mage fbaby medu, ntab
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .7514558 -.2780762 -.6065725 .4584385 1.335944
mage | 25.16667 26.81048 -1.643815 -.2925445 5.619026 .8818025
fbaby | .3715278 .4531498 -.081622 -.1644978 .4961893 .9430944
medu | 11.63889 12.92986 -1.290968 -.5121546 2.520661 .7315846
-------------------------------------------------------------------------------
Mean standardized diff. = -0.39394
Mean absolute standardized diff. = 0.39394
Maximum absolute standardized diff. = 0.60657
New variables created: _weight _weight_mtch _pscore _treated
-------------------------------------------------------
| Treatment Control Total
----------------------+--------------------------------
Number of rows | 864 3778 4642
Sum of sample weights | 864 3778 4642
Sum of weights | 864 3778 4642
-------------------------------------------------------
.
. // Estimate the average treatment effect of smoking on birthweight, using a logit model to predict treatment status
. psweight ipw mbsmoke mmarried mage fbaby medu
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
Propensity score model coefficients Number of obs = 4,642
Propensity score reweigting
Loss = IPW
------------------------------------------------------------------------------
mbsmoke | Coef.
-------------+----------------------------------------------------------------
mmarried | -1.036252
mage | -.0036626
fbaby | -.4765406
medu | -.127238
_cons | 1.024681
------------------------------------------------------------------------------
New variables created: _weight _weight_mtch _pscore _treated
. psweight call balanceresults()
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
mmarried | .6865574 .6976686 -.0111112 -.0241441 .4602022 1.021152
mage | 26.03507 26.47091 -.4358363 -.0769175 5.666282 .8180025
fbaby | .4485481 .4382549 .0102932 .0207332 .496461 1.005631
medu | 12.28844 12.62439 -.3359562 -.125102 2.685459 .5000611
-------------------------------------------------------------------------------
Mean standardized diff. = -0.05136
Mean absolute standardized diff. = 0.06172
Maximum absolute standardized diff. = 0.12510
C.V. of matching weights: = 0.28260
S.D. of matching weights: = 0.28260
Skewness of matching weights: = 0.03601
Kurtosis of matching weights: = 0.00341
Maximum matching weight: = 3.29874
.
. psweight call mystddiff = stddiff()
. mata: mystddiff
1 2 3 4
+-------------------------------------------------------------+
1 | -.0241441463 -.076917512 .020733199 -.1251019701 |
+-------------------------------------------------------------+
.
. // Estimate the average treatment effect on the treated with CBPS
. psweight cbps mbsmoke mmarried mage fbaby medu, atet
Step 1 (initial values from logit model):
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
Step 2 (CBPS) :
Iteration 0: f(p) = 5.800e-11
Iteration 1: f(p) = 5.705e-11
Iteration 2: f(p) = 4.419e-11
Iteration 3: f(p) = 3.123e-11
Iteration 4: f(p) = 2.530e-11
Iteration 5: f(p) = 1.774e-11
Iteration 6: f(p) = 1.666e-11
Iteration 7: f(p) = 8.483e-12
Iteration 8: f(p) = 2.502e-12
Iteration 9: f(p) = 2.123e-12
Iteration 10: f(p) = 8.205e-13
Iteration 11: f(p) = 3.774e-14
Iteration 12: f(p) = 1.171e-16
Iteration 13: f(p) = 1.190e-18
Iteration 14: f(p) = 7.414e-21
Propensity score model coefficients Number of obs = 4,642
Propensity score reweigting
Loss = CBPS (just identified)
------------------------------------------------------------------------------
mbsmoke | Coef.
-------------+----------------------------------------------------------------
mmarried | -1.079169
mage | -.0027934
fbaby | -.4962329
medu | -.0967742
_cons | .6621757
------------------------------------------------------------------------------
New variables created: _weight _weight_mtch _pscore _treated
. psweight call balanceresults()
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .4733793 3.25e-07 6.51e-07 .4993446 1.000894
mage | 25.16667 25.16667 -2.25e-06 -3.82e-07 5.888802 .7765934
fbaby | .3715278 .3715275 2.86e-07 5.93e-07 .4832651 1.000894
medu | 11.63889 11.63889 1.09e-06 3.57e-07 3.047441 .4545646
-------------------------------------------------------------------------------
Mean standardized diff. = 0.00000
Mean absolute standardized diff. = 0.00000
Maximum absolute standardized diff. = 0.00000
C.V. of matching weights: = 0.81153
S.D. of matching weights: = 0.81153
Skewness of matching weights: = 0.04626
Kurtosis of matching weights: = 0.00464
Maximum matching weight: = 8.08544
.
. // Estimate the average treatment effect on the treated with Penalized CBPS
. psweight pcbps mbsmoke mmarried mage fbaby medu, atet cvtarget(1 .5 6)
Step 1 (initial values from logit model):
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
Step 2 (CBPS) :
Iteration 0: f(p) = .00683679
Iteration 1: f(p) = 4.220e-06
Iteration 2: f(p) = 1.820e-07
Iteration 3: f(p) = 1.820e-07
Iteration 4: f(p) = 1.038e-07
Iteration 5: f(p) = 1.760e-08
Iteration 6: f(p) = 1.257e-08
Iteration 7: f(p) = 1.237e-08
Iteration 8: f(p) = 1.176e-08
Iteration 9: f(p) = 1.125e-08
Iteration 10: f(p) = 1.122e-08
Iteration 11: f(p) = 1.122e-08
Iteration 12: f(p) = 1.122e-08
Iteration 13: f(p) = 1.080e-08
Iteration 14: f(p) = 5.482e-09
Iteration 15: f(p) = 7.642e-10
Iteration 16: f(p) = 6.450e-10
Iteration 17: f(p) = 6.146e-10
Iteration 18: f(p) = 6.102e-10
Iteration 19: f(p) = 6.024e-10
Iteration 20: f(p) = 6.020e-10
Iteration 21: f(p) = 6.017e-10
Iteration 22: f(p) = 6.010e-10
Iteration 23: f(p) = 5.933e-10
Iteration 24: f(p) = 5.892e-10
Iteration 25: f(p) = 5.886e-10
Iteration 26: f(p) = 5.881e-10
Iteration 27: f(p) = 5.881e-10
Iteration 28: f(p) = 5.881e-10
Iteration 29: f(p) = 5.881e-10
Iteration 30: f(p) = 5.881e-10
Iteration 31: f(p) = 5.881e-10
Iteration 32: f(p) = 5.881e-10
Iteration 33: f(p) = 5.608e-10
Iteration 34: f(p) = 4.903e-10
Iteration 35: f(p) = 4.504e-10
Iteration 36: f(p) = 4.424e-10
Iteration 37: f(p) = 4.358e-10
Iteration 38: f(p) = 4.350e-10
Iteration 39: f(p) = 4.347e-10
Iteration 40: f(p) = 4.346e-10
Iteration 41: f(p) = 4.346e-10
Iteration 42: f(p) = 4.346e-10
Iteration 43: f(p) = 4.346e-10
Iteration 44: f(p) = 4.346e-10
Iteration 45: f(p) = 4.346e-10
Iteration 46: f(p) = 4.345e-10
Iteration 47: f(p) = 4.221e-10
Iteration 48: f(p) = 3.377e-10
Iteration 49: f(p) = 2.689e-10
Iteration 50: f(p) = 2.587e-10
Iteration 51: f(p) = 2.503e-10
Iteration 52: f(p) = 2.473e-10
Iteration 53: f(p) = 2.459e-10
Iteration 54: f(p) = 2.459e-10
Iteration 55: f(p) = 2.459e-10
Iteration 56: f(p) = 2.459e-10
Propensity score model coefficients Number of obs = 4,642
Propensity score reweigting
Loss = CBPS (just identified) + 1*abs(wgt_cv()-.5)^6)
------------------------------------------------------------------------------
mbsmoke | Coef.
-------------+----------------------------------------------------------------
mmarried | -.7698567
mage | -.0071298
fbaby | -.3632981
medu | -.0538343
_cons | -.0291464
------------------------------------------------------------------------------
New variables created: _weight _weight_mtch _pscore _treated
. psweight call balanceresults()
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .5602882 -.0869086 -.1744789 .498104 1.012781
mage | 25.16667 25.57391 -.4072411 -.0700995 5.809475 .8025661
fbaby | .3715278 .396575 -.0250472 -.0513021 .48823 .9766
medu | 11.63889 12.15997 -.5210767 -.1898107 2.745244 .5775639
-------------------------------------------------------------------------------
Mean standardized diff. = -0.12142
Mean absolute standardized diff. = 0.12142
Maximum absolute standardized diff. = 0.18981
C.V. of matching weights: = 0.51226
S.D. of matching weights: = 0.51226
Skewness of matching weights: = 0.02582
Kurtosis of matching weights: = 0.00151
Maximum matching weight: = 4.07165
.
.
. ************************************************************************************
. * Examples in the help file (psweight_class.sthlp)
. ************************************************************************************
.
.
. // Setup
. webuse cattaneo2, clear
(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154)
. gen byte touse=1
.
. mata:
------------------------------------------------- mata (type end to exit) --------------------------------------------------------------------------------------
:
: // Create an instance of the class, tell it where the data are
: P = psweight()
: P.st_set("mbsmoke", "mmarried mage fbaby medu", "touse")
:
: // Balance before reweighting
: P.balancetable(2)
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .7514558 -.2780762 -.6065725 .4584385 1.335944
mage | 25.16667 26.81048 -1.643815 -.2925445 5.619026 .8818025
fbaby | .3715278 .4531498 -.081622 -.1644978 .4961893 .9430944
medu | 11.63889 12.92986 -1.290968 -.5121546 2.520661 .7315846
-------------------------------------------------------------------------------
Mean standardized diff. = -0.39394
Mean absolute standardized diff. = 0.39394
Maximum absolute standardized diff. = 0.60657
1 2 3 4 5 6
+-------------------------------------------------------------------------------------------+
1 | .4733796296 .7514557967 -.2780761671 -.6065725095 .4584384599 1.335943589 |
2 | 25.16666667 26.81048174 -1.64381507 -.2925444767 5.619026168 .8818025263 |
3 | .3715277778 .4531498147 -.0816220369 -.1644977841 .4961892793 .9430944404 |
4 | 11.63888889 12.92985707 -1.290968178 -.5121545711 2.520661244 .7315846294 |
+-------------------------------------------------------------------------------------------+
:
: // Estimate the average treatment effect of smoking on birthweight, using a logit model to predict treatment status
: P.ipw()
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
1 2 3 4 5
+----------------------------------------------------------------------------+
1 | -1.036251862 -.003662621 -.476540647 -.1272379645 1.024680503 |
+----------------------------------------------------------------------------+
: P.balanceresults("ate", 1)
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_T var_ratio
-------------+-----------------------------------------------------------------
mmarried | .6865574 .6976686 -.0111112 -.0239382 .4641613 1.021152
mage | 26.03507 26.47091 -.4358363 -.0836215 5.21201 .8180025
fbaby | .4485481 .4382549 .0102932 .0206843 .4976337 1.005631
medu | 12.28844 12.62439 -.3359562 -.1686665 1.991838 .5000611
-------------------------------------------------------------------------------
Mean standardized diff. = -0.06389
Mean absolute standardized diff. = 0.07423
Maximum absolute standardized diff. = 0.16867
C.V. of matching weights: = 0.28260
S.D. of matching weights: = 0.28260
Skewness of matching weights: = 0.03601
Kurtosis of matching weights: = 0.00341
Maximum matching weight: = 3.29874
:
: // Estimate the average treatment effect on the treated with CBPS
: P.cbps("atet")
Step 1 (initial values from logit model):
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
Step 2 (CBPS) :
Iteration 0: f(p) = 5.800e-11
Iteration 1: f(p) = 5.705e-11
Iteration 2: f(p) = 4.419e-11
Iteration 3: f(p) = 3.123e-11
Iteration 4: f(p) = 2.530e-11
Iteration 5: f(p) = 1.774e-11
Iteration 6: f(p) = 1.666e-11
Iteration 7: f(p) = 8.483e-12
Iteration 8: f(p) = 2.502e-12
Iteration 9: f(p) = 2.123e-12
Iteration 10: f(p) = 8.205e-13
Iteration 11: f(p) = 3.774e-14
Iteration 12: f(p) = 1.171e-16
Iteration 13: f(p) = 1.190e-18
Iteration 14: f(p) = 7.414e-21
1 2 3 4 5
+----------------------------------------------------------------------------+
1 | -1.079169095 -.0027934281 -.4962329208 -.0967741508 .6621757391 |
+----------------------------------------------------------------------------+
: P.balanceresults("atet", 1)
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_T var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .4733793 3.25e-07 6.51e-07 .49958 1.000894
mage | 25.16667 25.16667 -2.25e-06 -4.24e-07 5.301348 .7765934
fbaby | .3715278 .3715275 2.86e-07 5.93e-07 .483493 1.000894
medu | 11.63889 11.63889 1.09e-06 5.02e-07 2.167743 .4545646
-------------------------------------------------------------------------------
Mean standardized diff. = 0.00000
Mean absolute standardized diff. = 0.00000
Maximum absolute standardized diff. = 0.00000
C.V. of matching weights: = 0.81153
S.D. of matching weights: = 0.81153
Skewness of matching weights: = 0.04626
Kurtosis of matching weights: = 0.00464
Maximum matching weight: = 8.08544
:
: // Estimate the average treatment effect on the treated with Penalized CBPS
: P.solve("atet", "cbps", 2, (1, .5, 6))
Step 1 (initial values from logit model):
initial: f(p) = -3217.5892
alternative: f(p) = -2632.6654
rescale: f(p) = -2317.1998
Iteration 0: f(p) = -2317.1998
Iteration 1: f(p) = -2156.2731
Iteration 2: f(p) = -2058.0577
Iteration 3: f(p) = -2057.5952
Iteration 4: f(p) = -2057.5951
Step 2 (CBPS) :
Iteration 0: f(p) = .00683679
Iteration 1: f(p) = 4.220e-06
Iteration 2: f(p) = 1.820e-07
Iteration 3: f(p) = 1.820e-07
Iteration 4: f(p) = 1.038e-07
Iteration 5: f(p) = 1.760e-08
Iteration 6: f(p) = 1.257e-08
Iteration 7: f(p) = 1.237e-08
Iteration 8: f(p) = 1.176e-08
Iteration 9: f(p) = 1.125e-08
Iteration 10: f(p) = 1.122e-08
Iteration 11: f(p) = 1.122e-08
Iteration 12: f(p) = 1.122e-08
Iteration 13: f(p) = 1.080e-08
Iteration 14: f(p) = 5.482e-09
Iteration 15: f(p) = 7.642e-10
Iteration 16: f(p) = 6.450e-10
Iteration 17: f(p) = 6.146e-10
Iteration 18: f(p) = 6.102e-10
Iteration 19: f(p) = 6.024e-10
Iteration 20: f(p) = 6.020e-10
Iteration 21: f(p) = 6.017e-10
Iteration 22: f(p) = 6.010e-10
Iteration 23: f(p) = 5.933e-10
Iteration 24: f(p) = 5.892e-10
Iteration 25: f(p) = 5.886e-10
Iteration 26: f(p) = 5.881e-10
Iteration 27: f(p) = 5.881e-10
Iteration 28: f(p) = 5.881e-10
Iteration 29: f(p) = 5.881e-10
Iteration 30: f(p) = 5.881e-10
Iteration 31: f(p) = 5.881e-10
Iteration 32: f(p) = 5.881e-10
Iteration 33: f(p) = 5.608e-10
Iteration 34: f(p) = 4.903e-10
Iteration 35: f(p) = 4.504e-10
Iteration 36: f(p) = 4.424e-10
Iteration 37: f(p) = 4.358e-10
Iteration 38: f(p) = 4.350e-10
Iteration 39: f(p) = 4.347e-10
Iteration 40: f(p) = 4.346e-10
Iteration 41: f(p) = 4.346e-10
Iteration 42: f(p) = 4.346e-10
Iteration 43: f(p) = 4.346e-10
Iteration 44: f(p) = 4.346e-10
Iteration 45: f(p) = 4.346e-10
Iteration 46: f(p) = 4.345e-10
Iteration 47: f(p) = 4.221e-10
Iteration 48: f(p) = 3.377e-10
Iteration 49: f(p) = 2.689e-10
Iteration 50: f(p) = 2.587e-10
Iteration 51: f(p) = 2.503e-10
Iteration 52: f(p) = 2.473e-10
Iteration 53: f(p) = 2.459e-10
Iteration 54: f(p) = 2.459e-10
Iteration 55: f(p) = 2.459e-10
Iteration 56: f(p) = 2.459e-10
1 2 3 4 5
+----------------------------------------------------------------------------+
1 | -.7698566831 -.0071297919 -.3632980692 -.0538343295 -.029146411 |
+----------------------------------------------------------------------------+
: P.balanceresults("atet", 1)
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_T var_ratio
-------------+-----------------------------------------------------------------
mmarried | .4733796 .5602882 -.0869086 -.1739633 .49958 1.012781
mage | 25.16667 25.57391 -.4072411 -.0768184 5.301348 .8025661
fbaby | .3715278 .396575 -.0250472 -.0518048 .483493 .9766
medu | 11.63889 12.15997 -.5210767 -.2403775 2.167743 .5775639
-------------------------------------------------------------------------------
Mean standardized diff. = -0.13574
Mean absolute standardized diff. = 0.13574
Maximum absolute standardized diff. = 0.24038
C.V. of matching weights: = 0.51226
S.D. of matching weights: = 0.51226
Skewness of matching weights: = 0.02582
Kurtosis of matching weights: = 0.00151
Maximum matching weight: = 4.07165
:
: end // end of Mata
----------------------------------------------------------------------------------------------------------------------------------------------------------------
.
. } // end of simple examples
.
.
. ************************************************************************************
. * Describe/summarize the example datasets
. ************************************************************************************
.
. *** Input data file (simple_cattaneo_data) comes from the program named Make_example_datasets.do (in C:\Users\kkranker\Documents\Stata\Multiple-Equation-Model
> s)
.
. use "C:\Users\kkranker\Documents\Stata\Multiple-Equation-Models\simple_cattaneo_data.dta", clear
(Cattaneo (2010) study on the effect of maternal smoking on birth weight)
. desc, short
Contains data from C:\Users\kkranker\Documents\Stata\Multiple-Equation-Models\simple_cattaneo_data.dta
obs: 4,642 Cattaneo (2010) study on the effect of maternal smoking on birth weight
vars: 11 8 Apr 2015 05:07
Sorted by:
. notes _dta
_dta:
1. Cattaneo (2010) study on the effect of maternal smoking on birth weight
2. Dataset created by Keith Kranker (program name: make_example_datasets.do)
3. Source: Cattaneo, M. D. 2010. "Efficient Semiparametric Estimation of Multi-Valued Treatment Effects under Ignorability." Journal of Econometrics, 155(2):
138154, doi:10.1016/j.jeconom.2009.09.023.
4. Source: Almond, D., Chay, K.Y., Lee, D.S., 2005. "The costs of low birth weight." Quarterly Journal of Economics, 120: 1031-1083.
5. Original dataset downloaded 4/3/2015 via Stata command ". webuse cattaneo2"
6. The variable names in the original datasets are replaced with "generic" names: (1) The outcome variables are named y1 y2 y3... (2) The treatment variable
is named treat (binary) or treat_cat (categorical). (3) The control variables are named x1 x2 x3...
. summ, sep(0)
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
treat | 4,642 .1861267 .3892508 0 1
treat_cat | 4,642 .3996122 .8988632 0 3
x1 | 4,642 .6996984 .4584385 0 1
x2 | 4,642 .0340371 .1813439 0 1
x3 | 4,642 .0534252 .2249042 0 1
x4 | 4,642 .4379578 .4961893 0 1
x5 | 4,642 26.50452 5.619026 13 45
x6 | 4,642 12.68957 2.520661 0 17
x7 | 4,642 6.540069 3.359167 1 12
y1 | 4,642 3361.68 578.8196 340 5500
y1_binary | 4,642 .9396812 .2381022 0 1
. tab treat treat_cat, mi
| treat_cat
treat | Untreated 1st Treat 2nd Treat 3rd Treat | Total
-----------+--------------------------------------------+----------
Untreated | 3,778 0 0 0 | 3,778
Treated | 0 200 337 327 | 864
-----------+--------------------------------------------+----------
Total | 3,778 200 337 327 | 4,642
. corr treat y1 y1_binary
(obs=4,642)
| treat y1 y1_bin~y
-------------+---------------------------
treat | 1.0000
y1 | -0.1851 1.0000
y1_binary | -0.0997 0.5924 1.0000
.
. local if if _n<=500
. set seed 1
. gen wgt = max(.1, rnormal(2,.4))
. gen fwgt = round(rnormal(2,.4))
. // forvalues i = 20/200 {
. forvalues i = 90/95 {
2. gen x`i' = rnormal()
3. }
.
. local depvarlist = "y1 y1_binary"
. local treatvar = "treat"
. local varlist = "x1 i.x2 i.x3 x4 x5 x6 x7 x9*"
. local wgtvar = "wgt"
. local tousevar = "touse"
. local estimate = "atet"
.
.
. // some automatic parsing based on options above, since Mata doesn't have this stuff
. if "`wgtvar'"!="" local wgtexp "[iw=`wgtvar']"
. mark `tousevar' `if' `in' `wgtexp'
. markout `tousevar' `depvarlist' `treatvar' `varlist'
. local varlist_orig : copy local varlist
.
. _rmcoll `treatvar' `varlist' if `tousevar' `wgtexp', expand logit touse(`tousevar')
. local varlist `r(varlist)'
. gettoken trash varlist : varlist
.
.
. // *******************************
. // * RUN MODELS DIRECTLY IN MATA *
. // *******************************
.
. if 1 {
.
. mata:
------------------------------------------------- mata (type end to exit) --------------------------------------------------------------------------------------
:
: depvarlist = st_local("depvarlist")
: treatvar = st_local("treatvar")
: wgtvar = st_local("wgtvar")
: varlist = st_local("varlist")
: tousevar = st_local("tousevar")
: estimate = st_local("estimateerror")
: // * UNWEIGHTED DATA EXAMPLES *
:
: D = psweight()
: D.st_set(treatvar, varlist, tousevar)
: if (depvarlist!="") D.st_set_depvars(depvarlist, tousevar)
>
> // Misc balance measures
> D.diff()
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -.2826086957 0 -.0160912191 0 -.0223785166 -.0603154305 -1.101236147 -1.289748508 .1626172208 .1457104569
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
----------------------------------------------------------------------------+
1 -.0812723301 .0681588142 .0461833248 -.1459731942 -.0148968728 |
----------------------------------------------------------------------------+
: D.stddiff()
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -.6149214017 . -.1050324967 . -.1140856324 -.1205719184 -.1896218542 -.5792408015 .0485796206 .1481119269
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
----------------------------------------------------------------------------+
1 -.0817632833 .0671946445 .0471721373 -.148808753 -.0150884239 |
----------------------------------------------------------------------------+
: D.stddiff(1)
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -.563336463 . -.1543415516 . -.1526193687 -.1210072453 -.202287272 -.8035533326 .0457213254 .1527707004
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
----------------------------------------------------------------------------+
1 -.08618905 .0648405717 .0493954389 -.1578414414 -.016298459 |
----------------------------------------------------------------------------+
: D.mean_asd(1)
.1977078631
: D.stddiff(1)
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -.563336463 . -.1543415516 . -.1526193687 -.1210072453 -.202287272 -.8035533326 .0457213254 .1527707004
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
----------------------------------------------------------------------------+
1 -.08618905 .0648405717 .0493954389 -.1578414414 -.016298459 |
----------------------------------------------------------------------------+
: D.stddiff(0)
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -.6518565108 . -.0992258067 . -.1088403741 -.120488729 -.1874830308 -.5658268397 .0492441608 .1472428581
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
----------------------------------------------------------------------------+
1 -.0808153611 .0677010963 .0466680215 -.1471098503 -.0148355423 |
----------------------------------------------------------------------------+
: D.varratio()
1 2 3 4 5 6 7 8 9 10
+---------------------------------------------------------------------------------------------------------------------------------------------
1 | 1.338962118 . .4133172708 . .5085817193 .9914483579 .8589874442 .4958355659 1.160037045 .9289414423
+---------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15
-----------------------------------------------------------------------+
1 .8791918375 1.090178798 .8926168505 .8686432131 .8285405428 |
-----------------------------------------------------------------------+
: D.progdiff(1)
------------------------------------------------------------------------
| mean_yhat_T mean_yhat_C diff std_diff mean_y_C
-------------+----------------------------------------------------------
y1 | 3444.538 3460.475 -15.93746 -.1234546 3460.475
y1_binary | .9482656 .9607843 -.0125187 -.3307945 .9607843
------------------------------------------------------------------------
Note: The std_diff column does not account for the standard error of the linear predictions.
1 2
+-------------------------------+
1 | -15.93745522 -.0125187159 |
+-------------------------------+
: D.get_N(1)
-------------------------------------------------------
| Treatment Control Total
----------------------+--------------------------------
Number of rows | 92 408 500
Sum of sample weights | 92 408 500
Sum of weights | 92 408 500
-------------------------------------------------------
1 2 3
+-------------------+
1 | 92 408 500 |
2 | 92 408 500 |
3 | 92 408 500 |
+-------------------+
:
: "Balance table before matching"
Balance table before matching
: table = D.balancetable(1)
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_T var_ratio
-------------+-----------------------------------------------------------------
x1 | .4673913 .75 -.2826087 -.5633365 .5016695 1.338962
|
x2 |
0 | (empty)
1 | .0108696 .0269608 -.0160912 -.1543416 .1042572 .4133173
|
x3 |
0 | (empty)
1 | .0217391 .0441176 -.0223785 -.1526194 .1466296 .5085817
|
x4 | .4347826 .495098 -.0603154 -.1210072 .4984448 .9914484
x5 | 25.46739 26.56863 -1.101236 -.2022873 5.443922 .8589874
x6 | 11.86957 13.15931 -1.289749 -.8035533 1.605057 .4958356
x7 | 6.858696 6.696078 .1626172 .0457213 3.556704 1.160037
x90 | .0972024 -.048508 .1457105 .1527707 .9537854 .9289414
x91 | -.0890157 -.0077434 -.0812723 -.0861891 .9429542 .8791918
x92 | .0453787 -.0227801 .0681588 .0648406 1.051175 1.090179
x93 | .0865687 .0403853 .0461833 .0493954 .9349714 .8926169
x94 | -.1299173 .0160559 -.1459732 -.1578414 .9248091 .8686432
x95 | -.0807251 -.0658282 -.0148969 -.0162985 .914005 .8285405
-------------------------------------------------------------------------------
Mean standardized diff. = -0.14960
Mean absolute standardized diff. = 0.19771
Maximum absolute standardized diff. = 0.80355
:
: // Replicate CBPS
:
: "--- ATE (not overidentified) ---"; ""; ""
--- ATE (not overidentified) ---
: stata(`"cbps `treatvar' `varlist' if `tousevar' , ate logit optimization_technique("nr") evaluator_type("gf1")"')
Iteration 0: f(p) = .00647386
Iteration 1: f(p) = .00451406
Iteration 2: f(p) = .0005336
Iteration 3: f(p) = .00001373
Iteration 4: f(p) = 2.637e-08
Iteration 5: f(p) = 6.777e-13
Iteration 6: f(p) = 1.066e-21
------------------------------------------------------------------------------
treat | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treat |
x1 | -1.441064 .5046768 -2.86 0.004 -2.430213 -.4519161
1.x2 | -2.548784 3.501511 -0.73 0.467 -9.41162 4.314051
1.x3 | -1.02318 2.005094 -0.51 0.610 -4.953091 2.906732
x4 | -.2320966 .5678014 -0.41 0.683 -1.344967 .8807737
x5 | .0736983 .042799 1.72 0.085 -.0101862 .1575829
x6 | -.4863391 .1923869 -2.53 0.011 -.8634106 -.1092676
x7 | .0655415 .0720901 0.91 0.363 -.0757525 .2068354
x90 | .0303861 .203675 0.15 0.881 -.3688096 .4295819
x91 | -.0236437 .2499136 -0.09 0.925 -.5134655 .466178
x92 | .2014652 .2092517 0.96 0.336 -.2086606 .6115911
x93 | -.0067202 .2511949 -0.03 0.979 -.4990531 .4856126
x94 | -.3662317 .2431817 -1.51 0.132 -.842859 .1103956
x95 | -.0080118 .2531324 -0.03 0.975 -.5041423 .4881187
_cons | 3.127763 1.826429 1.71 0.087 -.4519713 6.707497
------------------------------------------------------------------------------
: stata(`"cbps_imbalance"')
Total average covariate imbalance: 1.5e-10
Average covariate imbalance on the treated: 1.26
: D.cbps("ate", 2)
Step 1 (initial values from logit model):
initial: f(p) = -346.57359
alternative: f(p) = -283.03849
rescale: f(p) = -247.46401
Iteration 0: f(p) = -247.46401
Iteration 1: f(p) = -223.85141
Iteration 2: f(p) = -211.21543
Iteration 3: f(p) = -211.17754
Iteration 4: f(p) = -211.17753
Step 2 (CBPS) :
Iteration 0: f(p) = 2.590e-08
Iteration 1: f(p) = 2.548e-08 (backed up)
Iteration 2: f(p) = 1.980e-08
Iteration 3: f(p) = 1.652e-08
Iteration 4: f(p) = 1.627e-08
Iteration 5: f(p) = 1.540e-08
Iteration 6: f(p) = 1.451e-08
Iteration 7: f(p) = 1.377e-08
Iteration 8: f(p) = 1.290e-08
Iteration 9: f(p) = 1.195e-08
Iteration 10: f(p) = 1.168e-08
Iteration 11: f(p) = 1.075e-08
Iteration 12: f(p) = 1.026e-08
Iteration 13: f(p) = 1.005e-08
Iteration 14: f(p) = 9.509e-09
Iteration 15: f(p) = 9.074e-09
Iteration 16: f(p) = 6.361e-09
Iteration 17: f(p) = 2.702e-09
Iteration 18: f(p) = 2.272e-09
Iteration 19: f(p) = 1.867e-09
Iteration 20: f(p) = 1.339e-09
Iteration 21: f(p) = 1.242e-09
Iteration 22: f(p) = 1.149e-09
Iteration 23: f(p) = 9.784e-10
Iteration 24: f(p) = 9.420e-10
Iteration 25: f(p) = 9.360e-10
Iteration 26: f(p) = 9.200e-10
Iteration 27: f(p) = 9.147e-10
Iteration 28: f(p) = 9.058e-10
Iteration 29: f(p) = 7.129e-10
Iteration 30: f(p) = 3.744e-10
Iteration 31: f(p) = 2.987e-10
Iteration 32: f(p) = 2.963e-10
Iteration 33: f(p) = 2.653e-10
Iteration 34: f(p) = 9.786e-11
Iteration 35: f(p) = 7.971e-12
Iteration 36: f(p) = 7.291e-13
Iteration 37: f(p) = 7.440e-15
Iteration 38: f(p) = 1.955e-16
Iteration 39: f(p) = 1.491e-19
1 2 3 4 5 6 7 8 9 10
+-------------------------------------------------------------------------------------------------------------------------------------------------------
1 | -1.441063943 0 -2.548778665 0 -1.023181048 -.2320959429 .0736983529 -.4863391027 .065541404 .0303861341
+-------------------------------------------------------------------------------------------------------------------------------------------------------
11 12 13 14 15 16
-------------------------------------------------------------------------------------------+
1 -.0236438487 .201465 -.0067203894 -.3662317672 -.0080116617 3.127762038 |
-------------------------------------------------------------------------------------------+
: D.balanceresults("ate", 2)
Balance:
-------------------------------------------------------------------------------
| mean_T mean_C diff std_diff sd_pool var_ratio
-------------+-----------------------------------------------------------------
x1 | .6750468 .6750468 -3.26e-08 -6.96e-08 .4688264 1.008511
|
x2 |
0 | (empty)
1 | .0219356 .0219357 -9.38e-08 -6.39e-07 .1466203 1.008507
|
x3 |
0 | (empty)