Mirrors the core outputs of the old 07_propensity.R workflow.
Examples
# Run on a synthetic CPADS-shaped frame (the CKAN-fetched PUMF works
# identically -- see morie_load_cpads_data() for the real frame):
set.seed(1)
n <- 200
cpads <- data.frame(
weight = runif(n, 0.5, 2),
alcohol_past12m = rbinom(n, 1, 0.8),
heavy_drinking_30d = rbinom(n, 1, 0.3),
ebac_tot = abs(rnorm(n, 0.05, 0.03)),
ebac_legal = rbinom(n, 1, 0.7),
cannabis_any_use = rbinom(n, 1, 0.3),
age_group = sample(1:6, n, TRUE),
gender = sample(1:2, n, TRUE),
province_region = sample(1:5, n, TRUE),
mental_health = sample(1:5, n, TRUE),
physical_health = sample(1:5, n, TRUE)
)
morie_run_propensity_ipw_analysis(cpads)
#> $analysis_frame
#> cannabis_any_use heavy_drinking_30d age_group gender province_region
#> 1 0 0 4 2 5
#> 2 1 0 6 2 4
#> 3 1 1 5 1 5
#> 4 0 1 3 1 2
#> 5 1 1 3 1 3
#> 6 1 1 2 1 3
#> 7 1 0 2 1 3
#> 8 1 1 1 2 2
#> 9 0 0 5 1 3
#> 10 0 1 4 2 2
#> 11 0 1 2 2 4
#> 12 1 0 2 1 4
#> 13 0 1 3 1 4
#> 14 0 1 5 1 1
#> 15 0 1 3 1 2
#> 16 1 0 5 2 2
#> 17 1 0 4 2 4
#> 18 0 0 1 1 2
#> 19 0 0 6 2 4
#> 20 0 1 6 1 3
#> 21 0 0 5 2 5
#> 22 0 0 5 2 2
#> 23 0 0 1 1 1
#> 24 1 0 6 1 3
#> 25 0 0 1 2 5
#> 26 0 0 6 1 1
#> 27 0 0 2 1 4
#> 28 0 1 3 1 1
#> 29 1 1 2 2 4
#> 30 0 1 6 2 5
#> 31 0 1 3 1 5
#> 32 0 0 6 2 5
#> 33 0 0 3 1 2
#> 34 0 0 1 1 2
#> 35 0 0 1 2 1
#> 36 1 1 4 1 4
#> 37 0 1 1 2 2
#> 38 0 0 6 2 1
#> 39 0 0 4 1 3
#> 40 1 1 4 1 3
#> 41 0 0 1 1 1
#> 42 0 0 1 1 5
#> 43 0 0 1 1 5
#> 44 0 0 2 1 2
#> 45 1 0 1 2 3
#> 46 1 0 1 2 5
#> 47 1 0 5 1 4
#> 48 1 1 5 1 2
#> 49 1 0 1 2 1
#> 50 0 0 2 2 1
#> 51 0 1 1 2 1
#> 52 0 0 2 1 3
#> 53 1 0 6 2 1
#> 54 1 0 5 2 2
#> 55 0 0 1 2 3
#> 56 0 0 4 2 5
#> 57 0 0 4 2 2
#> 58 0 1 2 1 3
#> 59 1 0 5 2 4
#> 60 0 0 6 1 1
#> 61 0 0 2 1 1
#> 62 1 0 3 1 2
#> 63 0 0 1 1 3
#> 64 1 0 1 2 3
#> 65 0 0 5 2 2
#> 66 0 1 4 1 4
#> 67 0 1 6 1 5
#> 68 1 0 6 2 4
#> 69 0 0 1 2 1
#> 70 0 0 2 1 3
#> 71 0 0 1 1 5
#> 72 0 1 3 1 2
#> 73 1 1 2 2 3
#> 74 1 0 3 2 3
#> 75 0 0 6 2 3
#> 76 0 0 2 1 2
#> 77 0 1 1 1 3
#> 78 1 0 6 1 3
#> 79 1 0 4 2 4
#> 80 1 0 4 1 4
#> 81 0 1 1 2 2
#> 82 0 0 1 1 1
#> 83 0 1 5 1 3
#> 84 1 1 4 2 5
#> 85 1 0 2 1 2
#> 86 0 0 6 2 2
#> 87 0 0 5 1 1
#> 88 0 0 6 1 2
#> 89 0 1 5 2 2
#> 90 0 0 4 1 2
#> 91 0 0 3 2 5
#> 92 1 1 6 2 2
#> 93 1 0 5 1 4
#> 94 0 0 5 2 3
#> 95 0 0 2 1 3
#> 96 0 0 3 2 2
#> 97 0 0 1 2 3
#> 98 0 0 1 1 2
#> 99 0 1 6 1 3
#> 100 0 0 3 1 2
#> 101 0 0 5 1 4
#> 102 0 0 2 1 4
#> 103 0 0 5 2 4
#> 104 1 0 1 2 1
#> 105 1 0 3 2 3
#> 106 1 1 3 1 5
#> 107 0 0 3 2 4
#> 108 0 1 2 2 1
#> 109 0 1 3 1 1
#> 110 0 0 3 1 2
#> 111 0 1 6 1 3
#> 112 0 0 4 1 2
#> 113 0 0 3 1 4
#> 114 0 1 5 2 5
#> 115 0 1 3 2 5
#> 116 1 0 5 1 2
#> 117 0 0 5 1 2
#> 118 0 0 5 2 3
#> 119 1 0 5 2 1
#> 120 0 0 1 1 2
#> 121 0 1 5 1 1
#> 122 0 1 3 1 5
#> 123 1 0 1 2 2
#> 124 0 0 6 2 5
#> 125 0 0 1 1 4
#> 126 0 1 4 1 1
#> 127 0 1 2 2 2
#> 128 0 1 1 2 2
#> 129 0 1 1 2 1
#> 130 1 1 4 2 1
#> 131 0 0 3 2 2
#> 132 1 0 6 1 5
#> 133 0 0 3 2 5
#> 134 0 0 1 2 4
#> 135 1 0 4 2 2
#> 136 0 0 5 2 4
#> 137 0 0 5 1 3
#> 138 1 0 1 2 5
#> 139 1 0 3 1 3
#> 140 0 0 5 2 5
#> 141 0 0 3 1 1
#> 142 0 1 5 2 2
#> 143 1 0 3 1 1
#> 144 0 0 2 2 3
#> 145 0 0 5 2 4
#> 146 0 0 6 2 3
#> 147 0 1 5 1 2
#> 148 0 0 4 2 1
#> 149 1 0 2 2 5
#> 150 0 1 5 1 4
#> 151 0 1 6 1 2
#> 152 0 0 5 2 5
#> 153 0 0 1 2 3
#> 154 0 1 1 2 5
#> 155 0 1 1 1 5
#> 156 0 0 3 2 1
#> 157 0 0 4 2 2
#> 158 0 0 4 2 3
#> 159 0 1 5 1 3
#> 160 1 1 4 2 3
#> 161 1 0 2 2 5
#> 162 1 0 1 2 2
#> 163 0 0 3 1 1
#> 164 0 1 6 2 1
#> 165 0 1 4 2 5
#> 166 0 0 1 2 5
#> 167 0 0 2 1 5
#> 168 0 0 1 2 2
#> 169 0 0 2 2 2
#> 170 0 1 6 1 3
#> 171 0 0 2 2 5
#> 172 0 0 4 2 4
#> 173 0 0 6 2 3
#> 174 0 0 6 2 4
#> 175 1 1 5 1 3
#> 176 1 0 1 2 3
#> 177 1 0 6 2 5
#> 178 1 0 5 1 2
#> 179 0 1 4 1 4
#> 180 0 1 2 1 1
#> 181 1 0 4 2 1
#> 182 0 1 6 2 3
#> 183 0 0 4 1 1
#> 184 0 0 6 1 2
#> 185 0 0 6 1 5
#> 186 0 0 5 2 1
#> 187 0 0 3 2 3
#> 188 1 0 2 1 1
#> 189 0 1 4 1 3
#> 190 0 1 1 1 3
#> 191 0 0 2 1 3
#> 192 0 0 2 1 5
#> 193 0 1 5 1 5
#> 194 1 0 5 2 4
#> 195 1 1 3 2 1
#> 196 0 0 2 1 5
#> 197 1 0 3 2 4
#> 198 1 0 3 1 4
#> 199 0 0 5 2 5
#> 200 1 0 4 2 4
#> mental_health physical_health weight ps ipw ipw_trimmed
#> 1 1 2 0.8982630 0.2892950 1.407054 1.407054
#> 2 1 1 1.0581858 0.2958397 3.380209 3.380209
#> 3 2 3 1.3592800 0.2275214 4.395190 4.395190
#> 4 5 5 1.8623117 0.2935554 1.415539 1.415539
#> 5 5 2 0.8025229 0.3093136 3.232965 3.232965
#> 6 1 1 1.8475845 0.1641381 6.092430 4.786594
#> 7 5 4 1.9170129 0.2980574 3.355058 3.355058
#> 8 4 5 1.4911967 0.3542160 2.823136 2.823136
#> 9 1 3 1.4436711 0.1785068 1.217296 1.217296
#> 10 2 2 0.5926794 0.2974805 1.423448 1.423448
#> 11 1 4 0.8089619 0.2604561 1.352185 1.352185
#> 12 5 4 0.7648351 0.3092484 3.233646 3.233646
#> 13 2 3 1.5305343 0.2054560 1.258584 1.258584
#> 14 5 1 1.0761556 0.3047800 1.438394 1.438394
#> 15 2 3 1.6547621 0.1887126 1.232609 1.232609
#> 16 1 5 1.2465489 0.2609772 3.831752 3.831752
#> 17 3 5 1.5764278 0.3595990 2.780875 2.780875
#> 18 2 1 1.9878591 0.1793096 1.218486 1.218486
#> 19 2 2 1.0700528 0.3371061 1.508537 1.508537
#> 20 1 1 1.6661678 0.1864473 1.229177 1.229177
#> 21 4 2 1.9020578 0.4340474 1.766932 1.766932
#> 22 1 3 0.8182138 0.2638052 1.358336 1.358336
#> 23 3 1 1.4775106 0.2017029 1.252666 1.252666
#> 24 2 1 0.6883326 0.2184051 4.578648 4.578648
#> 25 4 5 0.9008310 0.3913198 1.642899 1.642899
#> 26 4 5 1.0791711 0.2662924 1.362941 1.362941
#> 27 2 3 0.5200855 0.1992226 1.248787 1.248787
#> 28 5 2 1.0735819 0.2871655 1.402850 1.402850
#> 29 4 2 1.8045363 0.3931342 2.543661 2.543661
#> 30 2 2 1.0105235 0.3490342 1.536179 1.536179
#> 31 4 5 1.2231202 0.2854340 1.399451 1.399451
#> 32 4 3 1.3993487 0.4417558 1.791331 1.791331
#> 33 3 4 1.2403120 0.2196982 1.281555 1.281555
#> 34 3 3 0.7793264 0.2079434 1.262536 1.262536
#> 35 5 4 1.7410600 0.3898588 1.638965 1.638965
#> 36 2 5 1.5027001 0.2094034 4.775471 4.775471
#> 37 5 5 1.6913598 0.4007643 1.668792 1.668792
#> 38 5 1 0.6619154 0.4420438 1.792255 1.792255
#> 39 5 2 1.5855664 0.3176252 1.465470 1.465470
#> 40 5 2 1.1169116 0.3176252 3.148365 3.148365
#> 41 4 5 1.7314194 0.2302975 1.299203 1.299203
#> 42 2 2 1.4705903 0.2026930 1.254222 1.254222
#> 43 4 3 1.6743991 0.2728324 1.375199 1.375199
#> 44 1 4 1.3295545 0.1541256 1.182209 1.190629
#> 45 4 5 1.2945794 0.3664149 2.729147 2.729147
#> 46 1 1 1.6840343 0.2674940 3.738402 3.738402
#> 47 2 4 0.5349968 0.2171087 4.605988 4.605988
#> 48 4 2 1.2158451 0.2734297 3.657247 3.657247
#> 49 4 1 1.5984706 0.3488121 2.866873 2.866873
#> 50 3 4 1.5390973 0.3087785 1.446714 1.446714
#> 51 4 1 1.2164294 0.3488121 1.535655 1.535655
#> 52 5 2 1.7918142 0.3011235 1.430868 1.430868
#> 53 1 5 1.1571457 0.2582273 3.872557 3.872557
#> 54 3 2 0.8671959 0.3492303 2.863440 2.863440
#> 55 1 2 0.6060186 0.2459110 1.326103 1.326103
#> 56 4 4 0.6491992 0.4210190 1.727172 1.727172
#> 57 2 2 0.9744076 0.2974805 1.423448 1.423448
#> 58 2 1 1.2779514 0.1931800 1.239434 1.239434
#> 59 2 1 1.4930076 0.3301438 3.028983 3.028983
#> 60 3 2 1.1102453 0.2332814 1.304259 1.304259
#> 61 2 1 1.8693139 0.1772139 1.215383 1.215383
#> 62 4 1 0.9404051 0.2597571 3.849750 3.849750
#> 63 3 3 1.1885986 0.2167960 1.276807 1.276807
#> 64 5 5 0.9985920 0.4135397 2.418147 2.418147
#> 65 1 5 1.4763057 0.2609772 1.353138 1.353138
#> 66 4 1 0.8870252 0.2884862 1.405454 1.405454
#> 67 4 3 1.2178179 0.3127739 1.455125 1.455125
#> 68 3 3 1.6494660 0.3810140 2.624575 2.624575
#> 69 2 2 0.6263704 0.2634479 1.357677 1.357677
#> 70 3 3 1.8129820 0.2234257 1.287707 1.287707
#> 71 4 3 1.0086094 0.2728324 1.375199 1.375199
#> 72 4 5 1.7591605 0.2541774 1.340801 1.340801
#> 73 3 2 1.0200252 0.3350652 2.984494 2.984494
#> 74 1 5 1.0006624 0.2563147 3.901454 3.901454
#> 75 2 5 1.2145269 0.3205892 1.471864 1.471864
#> 76 4 4 1.8382975 0.2482864 1.330294 1.330294
#> 77 4 4 1.7965092 0.2509662 1.335053 1.335053
#> 78 5 1 1.0849843 0.3362268 2.974183 2.974183
#> 79 5 3 1.6659810 0.4586101 2.180501 2.180501
#> 80 5 5 1.9409270 0.3243845 3.082761 3.082761
#> 81 3 4 1.1519892 0.3118404 1.453151 1.453151
#> 82 2 3 1.5687720 0.1695846 1.204217 1.204217
#> 83 3 2 1.0999916 0.2455224 1.325420 1.325420
#> 84 3 2 0.9880282 0.3770104 2.652446 2.652446
#> 85 5 3 1.6356307 0.2886007 3.464995 3.464995
#> 86 3 4 0.8040384 0.3547069 1.549683 1.549683
#> 87 4 5 1.5666818 0.2588146 1.349190 1.349190
#> 88 2 3 0.6825379 0.2070948 1.261185 1.261185
#> 89 1 4 0.8682328 0.2623887 1.355728 1.355728
#> 90 4 4 0.7149566 0.2629822 1.356819 1.356819
#> 91 1 2 0.8594441 0.2814190 1.391632 1.391632
#> 92 3 1 0.5884016 0.3597395 2.779789 2.779789
#> 93 5 3 1.4634324 0.3361588 2.974785 2.974785
#> 94 1 4 1.8144038 0.2727609 1.375064 1.375064
#> 95 1 5 1.6683720 0.1601680 1.190714 1.190714
#> 96 1 1 1.6959632 0.2518220 1.336580 1.336580
#> 97 2 2 1.1829117 0.2844968 1.397618 1.397618
#> 98 4 2 1.1151261 0.2438320 1.322457 1.322457
#> 99 4 2 1.7163054 0.2919903 1.412410 1.412410
#> 100 1 3 1.4073999 0.1602090 1.190772 1.190772
#> 101 2 5 1.4820859 0.2158694 1.275298 1.275298
#> 102 2 4 1.0297959 0.1980597 1.246976 1.246976
#> 103 5 1 0.9053902 0.4718538 1.893415 1.893415
#> 104 4 2 1.9890261 0.3471545 2.880562 2.880562
#> 105 3 1 1.4502399 0.3453744 2.895409 2.895409
#> 106 3 5 0.8198122 0.2467649 4.052440 4.052440
#> 107 3 2 0.6940585 0.3557611 1.552219 1.552219
#> 108 5 1 1.2171771 0.4043519 1.678844 1.678844
#> 109 2 5 1.8861117 0.1785886 1.217417 1.217417
#> 110 4 4 1.3981415 0.2555649 1.343300 1.343300
#> 111 4 4 1.9642560 0.2889788 1.406428 1.406428
#> 112 1 4 1.5976888 0.1644675 1.196841 1.196841
#> 113 3 4 1.0350904 0.2383829 1.312996 1.312996
#> 114 4 3 1.1472105 0.4322535 1.761350 1.761350
#> 115 4 2 0.7223173 0.4151772 1.709920 1.709920
#> 116 5 5 0.5196164 0.3098263 3.227615 3.227615
#> 117 2 3 1.5733491 0.2008243 1.251289 1.251289
#> 118 4 5 0.6547764 0.4029629 1.674938 1.674938
#> 119 4 1 1.1694265 0.3846703 2.599629 2.599629
#> 120 3 2 1.4601516 0.2091493 1.264461 1.264461
#> 121 2 5 1.9877579 0.1902029 1.234877 1.234877
#> 122 5 3 1.2433904 0.3307542 1.494219 1.494219
#> 123 2 5 1.2265243 0.2695096 3.710443 3.710443
#> 124 3 4 0.7601635 0.3918310 1.644280 1.644280
#> 125 5 2 1.6322314 0.3041429 1.437077 1.437077
#> 126 2 3 1.1808432 0.1865320 1.229305 1.229305
#> 127 5 4 1.2667547 0.4118429 1.700226 1.700226
#> 128 5 1 0.8113177 0.4078024 1.688625 1.688625
#> 129 3 2 0.8429872 0.3036773 1.436116 1.436116
#> 130 3 5 1.3935680 0.3239023 3.087350 3.087350
#> 131 5 5 1.3623083 0.4194492 1.722502 1.722502
#> 132 3 3 0.6155966 0.2718098 3.679043 3.679043
#> 133 3 2 0.5533109 0.3679829 1.582236 1.582236
#> 134 1 2 1.4641932 0.2558577 1.343829 1.343829
#> 135 3 3 1.8929228 0.3388656 2.951022 2.951022
#> 136 5 3 1.3971386 0.4682139 1.880455 1.880455
#> 137 2 3 1.3413511 0.2094539 1.264948 1.264948
#> 138 5 2 1.2890416 0.4448323 2.248038 2.248038
#> 139 4 1 1.9776428 0.2700633 3.702835 3.702835
#> 140 5 2 1.2614627 0.4832363 1.935121 1.935121
#> 141 5 1 1.5241821 0.2886633 1.405804 1.405804
#> 142 2 1 1.4023118 0.3071675 1.443350 1.443350
#> 143 5 1 0.8583030 0.2886633 3.464243 3.464243
#> 144 2 5 0.8872489 0.2879096 1.404316 1.404316
#> 145 3 2 1.5939644 0.3736561 1.596567 1.596567
#> 146 5 1 1.1788562 0.4682899 1.880724 1.880724
#> 147 3 1 0.7626902 0.2371693 1.310907 1.310907
#> 148 4 4 1.6200474 0.3704440 1.588421 1.588421
#> 149 5 5 0.6574815 0.4489615 2.227363 2.227363
#> 150 2 3 1.7968174 0.2183530 1.279350 1.279350
#> 151 5 5 1.4219675 0.3181453 1.466588 1.466588
#> 152 4 5 1.3357393 0.4286712 1.750306 1.750306
#> 153 3 5 0.9931660 0.3217142 1.474305 1.474305
#> 154 2 4 1.1796972 0.3034304 1.435607 1.435607
#> 155 5 3 1.2506615 0.3138842 1.457480 1.457480
#> 156 2 1 0.7712995 0.2801794 1.389235 1.389235
#> 157 2 5 1.2944459 0.2929204 1.414268 1.414268
#> 158 5 4 0.6129136 0.4436960 1.797578 1.797578
#> 159 4 3 0.9166339 0.2825867 1.393897 1.393897
#> 160 2 3 0.8190493 0.3071026 3.256241 3.256241
#> 161 5 4 0.9271857 0.4507696 2.218428 2.218428
#> 162 5 5 1.8426412 0.4007643 2.495232 2.495232
#> 163 4 2 1.1693530 0.2483433 1.330395 1.330395
#> 164 1 1 1.6699773 0.2638644 1.358445 1.358445
#> 165 2 1 1.8209286 0.3333154 1.499960 1.499960
#> 166 2 3 1.1196863 0.3049768 1.438801 1.438801
#> 167 4 5 0.5957127 0.2776220 1.384317 1.384317
#> 168 5 2 1.0032312 0.4060392 1.683613 1.683613
#> 169 1 3 1.5855889 0.2419252 1.319131 1.319131
#> 170 1 1 1.0064230 0.1864473 1.229177 1.229177
#> 171 1 4 1.4456212 0.2707799 1.371328 1.371328
#> 172 4 2 1.7609218 0.4117077 1.699835 1.699835
#> 173 5 5 1.7841975 0.4610206 1.855358 1.855358
#> 174 2 2 1.0870389 0.3371061 1.508537 1.508537
#> 175 4 2 1.0707408 0.2840702 3.520256 3.520256
#> 176 3 4 1.8431681 0.3233106 3.093001 3.093001
#> 177 4 4 1.4664736 0.4399549 2.272960 2.272960
#> 178 1 4 1.6116180 0.1698440 5.887755 4.786594
#> 179 2 5 1.4079552 0.2094034 1.264868 1.264868
#> 180 1 5 1.8546224 0.1464351 1.171557 1.190629
#> 181 4 3 0.9405952 0.3721494 2.687093 2.687093
#> 182 4 2 0.7868902 0.4176097 1.717061 1.717061
#> 183 5 4 1.8296764 0.2921057 1.412640 1.412640
#> 184 1 2 1.2550092 0.1774825 1.215780 1.215780
#> 185 2 2 1.8155863 0.2356946 1.308378 1.308378
#> 186 5 1 0.7837904 0.4325402 1.762239 1.762239
#> 187 5 5 1.6371546 0.4323905 1.761775 1.761775
#> 188 5 1 1.5867483 0.2807977 3.561282 3.561282
#> 189 2 1 1.9155872 0.2055058 1.258662 1.258662
#> 190 4 3 1.3214699 0.2523421 1.337510 1.337510
#> 191 5 1 1.5676158 0.3026632 1.434027 1.434027
#> 192 4 4 1.0833576 0.2790896 1.387135 1.387135
#> 193 2 1 0.6513097 0.2300998 1.298870 1.298870
#> 194 2 5 1.8909531 0.3237134 3.089152 3.089152
#> 195 1 1 0.9248488 0.2419811 4.132554 4.132554
#> 196 3 2 1.3858597 0.2436730 1.322179 1.322179
#> 197 2 3 0.6655409 0.3101553 3.224191 3.224191
#> 198 5 4 1.7607605 0.3175591 3.149020 3.149020
#> 199 3 5 0.9769455 0.3809421 1.615358 1.615358
#> 200 4 1 1.6742770 0.4134784 2.418506 2.418506
#>
#> $ipw_results
#> estimand method estimate n y1_ipw y0_ipw
#> 1 ATE IPW (trimmed) -0.05082881 200 0.286671 0.3374998
#>
#> $diagnostics
#> metric value
#> 1 ps_mean 0.3000000
#> 2 ps_min 0.1464351
#> 3 ps_max 0.4832363
#> 4 ipw_mean 1.9888181
#> 5 ipw_trimmed_mean 1.9769206
#> 6 ess_ipw_trimmed 164.2785641
#>