Explore your design
Print code to recreate a design
Arguments
- design
A design object, typically created using the + operator
- x
a design object, typically created using the + operator
- verbose
an indicator for printing a long summary of the design, defaults to
TRUE- ...
optional arguments to be sent to summary function
- object
a design object created using the + operator
Examples
# Two-arm randomized experiment
design <-
declare_model(
N = 500,
gender = rbinom(N, 1, 0.5),
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = 200)) +
declare_assignment(Z = complete_ra(N = N, m = 100)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
# Use draw_data to create a dataset using a design
dat <- draw_data(design)
draw_data(design, data = dat, start = 2)
#> ID gender X U Y_Z_0 Y_Z_1 S Z Y
#> 1 003 0 0 -0.150486326 -0.150486326 0.049513674 1 0 -0.150486326
#> 2 006 1 0 -0.084587862 -0.084587862 0.115412138 1 0 -0.084587862
#> 3 007 1 0 0.194461738 0.194461738 0.394461738 1 0 0.194461738
#> 4 009 1 0 0.213616023 0.213616023 0.413616023 1 0 0.213616023
#> 5 011 0 0 -0.013242846 -0.013242846 0.186757154 1 1 0.186757154
#> 6 012 0 0 -0.204212683 -0.204212683 -0.004212683 1 1 -0.004212683
#> 7 013 0 0 0.154427687 0.154427687 0.354427687 1 1 0.354427687
#> 8 014 0 0 -0.112189277 -0.112189277 0.087810723 1 1 0.087810723
#> 9 015 1 0 -0.259167049 -0.259167049 -0.059167049 1 0 -0.259167049
#> 10 017 0 0 -0.060665974 -0.060665974 0.139334026 1 0 -0.060665974
#> 11 021 1 0 0.038150194 0.038150194 0.238150194 1 1 0.238150194
#> 12 022 1 0 0.543230193 0.543230193 0.743230193 1 1 0.743230193
#> 13 024 0 0 -0.150472420 -0.150472420 0.049527580 1 1 0.049527580
#> 14 026 0 0 -0.125173485 -0.125173485 0.074826515 1 1 0.074826515
#> 15 028 1 0 0.158083990 0.158083990 0.358083990 1 1 0.358083990
#> 16 031 1 0 -0.037207535 -0.037207535 0.162792465 1 1 0.162792465
#> 17 033 1 0 0.216372199 0.216372199 0.416372199 1 1 0.416372199
#> 18 041 1 0 -0.302800988 -0.302800988 -0.102800988 1 0 -0.302800988
#> 19 044 1 0 0.063166772 0.063166772 0.263166772 1 1 0.263166772
#> 20 045 0 0 0.392432089 0.392432089 0.592432089 1 0 0.392432089
#> 21 047 1 0 -0.211267141 -0.211267141 -0.011267141 1 1 -0.011267141
#> 22 053 0 0 0.200910023 0.200910023 0.400910023 1 1 0.400910023
#> 23 054 1 0 -0.373313806 -0.373313806 -0.173313806 1 0 -0.373313806
#> 24 055 1 0 0.292453637 0.292453637 0.492453637 1 0 0.292453637
#> 25 062 0 0 -0.346026735 -0.346026735 -0.146026735 1 0 -0.346026735
#> 26 063 1 0 0.119288470 0.119288470 0.319288470 1 0 0.119288470
#> 27 066 1 0 0.128680071 0.128680071 0.328680071 1 1 0.328680071
#> 28 068 0 0 0.336870863 0.336870863 0.536870863 1 1 0.536870863
#> 29 074 1 0 0.335830139 0.335830139 0.535830139 1 0 0.335830139
#> 30 075 1 0 0.020228561 0.020228561 0.220228561 1 0 0.020228561
#> 31 076 0 0 -0.258645739 -0.258645739 -0.058645739 1 0 -0.258645739
#> 32 081 0 0 -0.063642148 -0.063642148 0.136357852 1 1 0.136357852
#> 33 082 1 0 0.611224653 0.611224653 0.811224653 1 1 0.811224653
#> 34 083 1 0 0.115876148 0.115876148 0.315876148 1 1 0.315876148
#> 35 087 0 0 -0.163010959 -0.163010959 0.036989041 1 0 -0.163010959
#> 36 090 0 0 0.210371064 0.210371064 0.410371064 1 1 0.410371064
#> 37 091 1 0 0.093532125 0.093532125 0.293532125 1 1 0.293532125
#> 38 092 0 0 0.309967308 0.309967308 0.509967308 1 0 0.309967308
#> 39 093 0 0 0.057506171 0.057506171 0.257506171 1 0 0.057506171
#> 40 098 0 0 -0.579069666 -0.579069666 -0.379069666 1 1 -0.379069666
#> 41 099 1 0 -0.100917158 -0.100917158 0.099082842 1 0 -0.100917158
#> 42 100 1 0 -0.323532267 -0.323532267 -0.123532267 1 0 -0.323532267
#> 43 101 0 0 0.269007461 0.269007461 0.469007461 1 1 0.469007461
#> 44 106 0 0 -0.285804918 -0.285804918 -0.085804918 1 0 -0.285804918
#> 45 108 0 0 -0.098915984 -0.098915984 0.101084016 1 0 -0.098915984
#> 46 110 0 0 0.102852340 0.102852340 0.302852340 1 0 0.102852340
#> 47 112 1 0 -0.246558941 -0.246558941 -0.046558941 1 1 -0.046558941
#> 48 120 0 0 -0.044145759 -0.044145759 0.155854241 1 1 0.155854241
#> 49 123 1 0 0.113026977 0.113026977 0.313026977 1 0 0.113026977
#> 50 124 0 0 0.554036640 0.554036640 0.754036640 1 0 0.554036640
#> 51 126 1 0 -0.110071367 -0.110071367 0.089928633 1 0 -0.110071367
#> 52 131 1 0 0.165169779 0.165169779 0.365169779 1 0 0.165169779
#> 53 133 1 0 0.235376348 0.235376348 0.435376348 1 1 0.435376348
#> 54 134 1 0 -0.154996338 -0.154996338 0.045003662 1 0 -0.154996338
#> 55 136 1 0 0.044519764 0.044519764 0.244519764 1 1 0.244519764
#> 56 137 1 0 0.376869001 0.376869001 0.576869001 1 0 0.376869001
#> 57 140 0 0 -0.024259690 -0.024259690 0.175740310 1 0 -0.024259690
#> 58 141 0 0 0.328723768 0.328723768 0.528723768 1 0 0.328723768
#> 59 143 1 0 -0.148174563 -0.148174563 0.051825437 1 0 -0.148174563
#> 60 150 1 0 0.217938047 0.217938047 0.417938047 1 1 0.417938047
#> 61 153 1 0 0.201303501 0.201303501 0.401303501 1 1 0.401303501
#> 62 157 0 0 -0.296321268 -0.296321268 -0.096321268 1 0 -0.296321268
#> 63 158 0 0 0.226451783 0.226451783 0.426451783 1 1 0.426451783
#> 64 162 1 0 0.141916145 0.141916145 0.341916145 1 1 0.341916145
#> 65 163 1 0 0.346803277 0.346803277 0.546803277 1 0 0.346803277
#> 66 169 1 0 0.452663549 0.452663549 0.652663549 1 0 0.452663549
#> 67 171 1 0 0.167137615 0.167137615 0.367137615 1 0 0.167137615
#> 68 176 0 0 -0.269437036 -0.269437036 -0.069437036 1 1 -0.069437036
#> 69 177 0 0 -0.118090636 -0.118090636 0.081909364 1 1 0.081909364
#> 70 178 0 0 0.010701505 0.010701505 0.210701505 1 0 0.010701505
#> 71 184 1 0 -0.097696388 -0.097696388 0.102303612 1 1 0.102303612
#> 72 185 1 0 0.282139647 0.282139647 0.482139647 1 1 0.482139647
#> 73 186 1 0 0.004699545 0.004699545 0.204699545 1 0 0.004699545
#> 74 188 1 0 -0.224135278 -0.224135278 -0.024135278 1 0 -0.224135278
#> 75 190 1 0 -0.003463190 -0.003463190 0.196536810 1 0 -0.003463190
#> 76 192 1 0 0.132768266 0.132768266 0.332768266 1 1 0.332768266
#> 77 200 0 0 0.326750321 0.326750321 0.526750321 1 0 0.326750321
#> 78 201 0 0 -0.369522095 -0.369522095 -0.169522095 1 0 -0.369522095
#> 79 202 1 0 -0.054320190 -0.054320190 0.145679810 1 0 -0.054320190
#> 80 204 0 0 0.452766465 0.452766465 0.652766465 1 1 0.652766465
#> 81 207 0 0 -0.087613663 -0.087613663 0.112386337 1 1 0.112386337
#> 82 208 0 0 0.157238360 0.157238360 0.357238360 1 0 0.157238360
#> 83 209 0 0 0.156817605 0.156817605 0.356817605 1 1 0.356817605
#> 84 210 0 0 0.106786788 0.106786788 0.306786788 1 0 0.106786788
#> 85 211 1 0 0.285225442 0.285225442 0.485225442 1 0 0.285225442
#> 86 219 0 0 0.231139552 0.231139552 0.431139552 1 1 0.431139552
#> 87 223 0 0 0.107620599 0.107620599 0.307620599 1 1 0.307620599
#> 88 225 0 0 0.225982875 0.225982875 0.425982875 1 1 0.425982875
#> 89 228 1 0 -0.067940607 -0.067940607 0.132059393 1 1 0.132059393
#> 90 238 0 0 -0.116158295 -0.116158295 0.083841705 1 1 0.083841705
#> 91 245 1 0 -0.140797314 -0.140797314 0.059202686 1 1 0.059202686
#> 92 247 0 0 0.118085070 0.118085070 0.318085070 1 1 0.318085070
#> 93 249 0 0 0.298908778 0.298908778 0.498908778 1 0 0.298908778
#> 94 251 1 1 0.382034269 1.382034269 1.582034269 1 1 1.582034269
#> 95 252 1 1 -0.036625508 0.963374492 1.163374492 1 0 0.963374492
#> 96 254 0 1 0.031560388 1.031560388 1.231560388 1 0 1.031560388
#> 97 255 1 1 -0.011660201 0.988339799 1.188339799 1 0 0.988339799
#> 98 257 0 1 0.333300076 1.333300076 1.533300076 1 1 1.533300076
#> 99 259 0 1 -0.088735418 0.911264582 1.111264582 1 0 0.911264582
#> 100 260 1 1 -0.335594465 0.664405535 0.864405535 1 0 0.664405535
#> 101 261 1 1 0.107553015 1.107553015 1.307553015 1 1 1.307553015
#> 102 263 0 1 -0.088524742 0.911475258 1.111475258 1 1 1.111475258
#> 103 266 0 1 -0.131663434 0.868336566 1.068336566 1 0 0.868336566
#> 104 277 1 1 0.198441226 1.198441226 1.398441226 1 0 1.198441226
#> 105 279 0 1 -0.010962140 0.989037860 1.189037860 1 1 1.189037860
#> 106 285 0 1 0.204915458 1.204915458 1.404915458 1 1 1.404915458
#> 107 289 1 1 -0.147299106 0.852700894 1.052700894 1 1 1.052700894
#> 108 299 0 1 0.193049598 1.193049598 1.393049598 1 0 1.193049598
#> 109 300 0 1 -0.173685832 0.826314168 1.026314168 1 0 0.826314168
#> 110 301 1 1 -0.049088480 0.950911520 1.150911520 1 0 0.950911520
#> 111 305 0 1 -0.054268542 0.945731458 1.145731458 1 0 0.945731458
#> 112 308 0 1 0.089151498 1.089151498 1.289151498 1 0 1.089151498
#> 113 310 1 1 -0.199640749 0.800359251 1.000359251 1 0 0.800359251
#> 114 312 0 1 0.060769489 1.060769489 1.260769489 1 0 1.060769489
#> 115 313 1 1 0.249679839 1.249679839 1.449679839 1 1 1.449679839
#> 116 314 0 1 -0.172034822 0.827965178 1.027965178 1 0 0.827965178
#> 117 316 1 1 0.191559560 1.191559560 1.391559560 1 1 1.391559560
#> 118 317 0 1 0.018177846 1.018177846 1.218177846 1 0 1.018177846
#> 119 318 0 1 0.279421615 1.279421615 1.479421615 1 0 1.279421615
#> 120 319 1 1 -0.025904472 0.974095528 1.174095528 1 1 1.174095528
#> 121 320 1 1 -0.088512977 0.911487023 1.111487023 1 1 1.111487023
#> 122 322 1 1 0.295311208 1.295311208 1.495311208 1 0 1.295311208
#> 123 323 1 1 -0.373858268 0.626141732 0.826141732 1 0 0.626141732
#> 124 324 1 1 0.344402375 1.344402375 1.544402375 1 1 1.544402375
#> 125 325 1 1 0.080625130 1.080625130 1.280625130 1 1 1.280625130
#> 126 327 1 1 -0.420480699 0.579519301 0.779519301 1 1 0.779519301
#> 127 329 1 1 -0.065528790 0.934471210 1.134471210 1 0 0.934471210
#> 128 330 0 1 -0.355408336 0.644591664 0.844591664 1 0 0.644591664
#> 129 337 1 1 0.348752098 1.348752098 1.548752098 1 0 1.348752098
#> 130 339 0 1 0.802114799 1.802114799 2.002114799 1 0 1.802114799
#> 131 340 1 1 0.214709232 1.214709232 1.414709232 1 0 1.214709232
#> 132 343 1 1 0.123516542 1.123516542 1.323516542 1 0 1.123516542
#> 133 348 1 1 0.177905769 1.177905769 1.377905769 1 1 1.377905769
#> 134 349 0 1 -0.226189796 0.773810204 0.973810204 1 0 0.773810204
#> 135 352 0 1 0.254914563 1.254914563 1.454914563 1 1 1.454914563
#> 136 357 1 1 0.219878968 1.219878968 1.419878968 1 1 1.419878968
#> 137 359 0 1 -0.217274487 0.782725513 0.982725513 1 1 0.982725513
#> 138 360 0 1 0.063897195 1.063897195 1.263897195 1 1 1.263897195
#> 139 361 1 1 -0.155604632 0.844395368 1.044395368 1 1 1.044395368
#> 140 363 1 1 0.522092460 1.522092460 1.722092460 1 0 1.522092460
#> 141 365 0 1 0.462182937 1.462182937 1.662182937 1 1 1.662182937
#> 142 368 1 1 -0.182284498 0.817715502 1.017715502 1 1 1.017715502
#> 143 369 1 1 -0.007262023 0.992737977 1.192737977 1 0 0.992737977
#> 144 370 1 1 -0.378084781 0.621915219 0.821915219 1 0 0.621915219
#> 145 371 1 1 -0.097472486 0.902527514 1.102527514 1 1 1.102527514
#> 146 372 1 1 0.029283442 1.029283442 1.229283442 1 1 1.229283442
#> 147 374 1 1 0.113630761 1.113630761 1.313630761 1 1 1.313630761
#> 148 375 1 1 0.117549924 1.117549924 1.317549924 1 1 1.317549924
#> 149 376 1 1 -0.108800123 0.891199877 1.091199877 1 0 0.891199877
#> 150 378 1 1 -0.089510414 0.910489586 1.110489586 1 0 0.910489586
#> 151 379 0 1 -0.022366564 0.977633436 1.177633436 1 1 1.177633436
#> 152 380 0 1 -0.013718727 0.986281273 1.186281273 1 1 1.186281273
#> 153 381 0 1 0.044950358 1.044950358 1.244950358 1 0 1.044950358
#> 154 382 1 1 -0.072541396 0.927458604 1.127458604 1 0 0.927458604
#> 155 383 1 1 0.266584853 1.266584853 1.466584853 1 1 1.466584853
#> 156 384 0 1 -0.410995252 0.589004748 0.789004748 1 0 0.589004748
#> 157 386 1 1 -0.100336198 0.899663802 1.099663802 1 1 1.099663802
#> 158 388 1 1 0.107647345 1.107647345 1.307647345 1 0 1.107647345
#> 159 389 1 1 -0.191118720 0.808881280 1.008881280 1 1 1.008881280
#> 160 398 1 1 -0.088252092 0.911747908 1.111747908 1 1 1.111747908
#> 161 399 1 1 0.228924189 1.228924189 1.428924189 1 1 1.428924189
#> 162 402 0 1 0.091484675 1.091484675 1.291484675 1 1 1.291484675
#> 163 404 1 1 0.104674353 1.104674353 1.304674353 1 1 1.304674353
#> 164 407 1 1 -0.148037522 0.851962478 1.051962478 1 0 0.851962478
#> 165 409 1 1 0.128628920 1.128628920 1.328628920 1 1 1.328628920
#> 166 410 1 1 0.044772815 1.044772815 1.244772815 1 1 1.244772815
#> 167 416 1 1 0.284272578 1.284272578 1.484272578 1 1 1.484272578
#> 168 417 0 1 -0.071230379 0.928769621 1.128769621 1 1 1.128769621
#> 169 418 1 1 -0.392666836 0.607333164 0.807333164 1 1 0.807333164
#> 170 419 1 1 -0.077320762 0.922679238 1.122679238 1 0 0.922679238
#> 171 427 1 1 -0.186389955 0.813610045 1.013610045 1 1 1.013610045
#> 172 428 0 1 0.298598646 1.298598646 1.498598646 1 0 1.298598646
#> 173 429 1 1 -0.447442309 0.552557691 0.752557691 1 0 0.552557691
#> 174 434 0 1 0.013670679 1.013670679 1.213670679 1 1 1.213670679
#> 175 441 0 1 -0.334447267 0.665552733 0.865552733 1 0 0.665552733
#> 176 443 1 1 0.275227849 1.275227849 1.475227849 1 1 1.475227849
#> 177 444 1 1 0.184667521 1.184667521 1.384667521 1 0 1.184667521
#> 178 446 0 1 -0.804189930 0.195810070 0.395810070 1 0 0.195810070
#> 179 447 0 1 0.084371395 1.084371395 1.284371395 1 1 1.284371395
#> 180 452 1 1 -0.685242321 0.314757679 0.514757679 1 1 0.514757679
#> 181 453 0 1 -0.214819259 0.785180741 0.985180741 1 1 0.985180741
#> 182 454 0 1 0.237461514 1.237461514 1.437461514 1 1 1.437461514
#> 183 457 1 1 -0.009345136 0.990654864 1.190654864 1 0 0.990654864
#> 184 458 1 1 -0.222411044 0.777588956 0.977588956 1 1 0.977588956
#> 185 461 1 1 0.183847586 1.183847586 1.383847586 1 0 1.183847586
#> 186 464 0 1 -0.291251800 0.708748200 0.908748200 1 1 0.908748200
#> 187 465 1 1 0.290636602 1.290636602 1.490636602 1 0 1.290636602
#> 188 466 1 1 -0.076736671 0.923263329 1.123263329 1 0 0.923263329
#> 189 467 0 1 -0.010250540 0.989749460 1.189749460 1 0 0.989749460
#> 190 470 0 1 -0.144326399 0.855673601 1.055673601 1 0 0.855673601
#> 191 471 1 1 -0.037118876 0.962881124 1.162881124 1 0 0.962881124
#> 192 477 1 1 -0.116227059 0.883772941 1.083772941 1 0 0.883772941
#> 193 478 0 1 -0.359779206 0.640220794 0.840220794 1 1 0.840220794
#> 194 482 0 1 -0.005264136 0.994735864 1.194735864 1 1 1.194735864
#> 195 483 0 1 -0.013593800 0.986406200 1.186406200 1 0 0.986406200
#> 196 485 1 1 0.062635383 1.062635383 1.262635383 1 1 1.262635383
#> 197 489 0 1 0.268376067 1.268376067 1.468376067 1 0 1.268376067
#> 198 493 1 1 0.124730950 1.124730950 1.324730950 1 1 1.324730950
#> 199 494 1 1 0.101322387 1.101322387 1.301322387 1 0 1.101322387
#> 200 497 1 1 0.208214183 1.208214183 1.408214183 1 1 1.408214183
# Apply get_estimates
get_estimates(design, data = dat)
#> estimator term estimate std.error statistic p.value conf.low
#> 1 estimator Z 0.05570193 0.07484884 0.7441923 0.4576424 -0.09190129
#> conf.high df outcome inquiry
#> 1 0.2033051 198 Y ATE
# Two-arm randomized experiment
design <-
declare_model(
N = 500,
gender = rbinom(N, 1, 0.5),
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = 200)) +
declare_assignment(Z = complete_ra(N = N, m = 100)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
print_code(design)
#> model <- declare_model(N = 500, gender = rbinom(N, 1, 0.5), X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> ATE <- declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0))
#>
#> sampling <- declare_sampling(S = complete_rs(N = N, n = 200))
#>
#> assignment <- declare_assignment(Z = complete_ra(N = N, m = 100))
#>
#> measurement <- declare_measurement(Y = reveal_outcomes(Y ~ Z))
#>
#> estimator <- declare_estimator(Y ~ Z, inquiry = "ATE")
#>
#> my_design <- construct_design(steps = steps)
#>
summary(design)
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, gender = rbinom(N, 1, 0.5), X = rep(c(0, 1), each = N/2), U = rnorm(N, sd = 0.25), potential_outcomes(Y ~ 0.2 * Z + X + U))
#>
#> N = 500
#>
#> Added variable: ID
#> N_missing N_unique class
#> 0 500 character
#>
#> Added variable: gender
#> 0 1
#> 237 263
#> 0.47 0.53
#>
#> Added variable: X
#> 0 1
#> 250 250
#> 0.50 0.50
#>
#> Added variable: U
#> min median mean max sd N_missing N_unique
#> -0.7 0 0 0.75 0.25 0 500
#>
#> Added variable: Y_Z_0
#> min median mean max sd N_missing N_unique
#> -0.7 0.46 0.5 1.66 0.55 0 500
#>
#> Added variable: Y_Z_1
#> min median mean max sd N_missing N_unique
#> -0.5 0.66 0.7 1.86 0.55 0 500
#>
#> Step 2 (inquiry): declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) -------------------
#>
#> A single draw of the inquiry:
#> inquiry estimand
#> ATE 0.2
#>
#> Step 3 (sampling): declare_sampling(S = complete_rs(N = N, n = 200)) -----------
#>
#> N = 200 (300 subtracted)
#>
#> Added variable: S
#> 1
#> 200
#> 1.00
#>
#> Altered variable: ID
#> Before:
#> N_missing N_unique class
#> 0 500 character
#>
#> After:
#> N_missing N_unique class
#> 0 200 character
#>
#> Altered variable: gender
#> Before:
#> 0 1
#> 237 263
#> 0.47 0.53
#>
#> After:
#> 0 1
#> 95 105
#> 0.47 0.53
#>
#> Altered variable: X
#> Before:
#> 0 1
#> 250 250
#> 0.50 0.50
#>
#> After:
#> 0 1
#> 100 100
#> 0.50 0.50
#>
#> Altered variable: U
#> Before:
#> min median mean max sd N_missing N_unique
#> -0.7 0 0 0.75 0.25 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -0.7 -0.02 -0.01 0.59 0.25 0 200
#>
#> Altered variable: Y_Z_0
#> Before:
#> min median mean max sd N_missing N_unique
#> -0.7 0.46 0.5 1.66 0.55 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -0.7 0.5 0.49 1.53 0.54 0 200
#>
#> Altered variable: Y_Z_1
#> Before:
#> min median mean max sd N_missing N_unique
#> -0.5 0.66 0.7 1.86 0.55 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -0.5 0.7 0.69 1.73 0.54 0 200
#>
#> Step 4 (assignment): declare_assignment(Z = complete_ra(N = N, m = 100)) -------
#>
#> Added variable: Z
#> 0 1
#> 100 100
#> 0.50 0.50
#>
#> Step 5 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Added variable: Y
#> min median mean max sd N_missing N_unique
#> -0.69 0.6 0.59 1.57 0.55 0 200
#>
#> Step 6 (estimator): declare_estimator(Y ~ Z, inquiry = "ATE") ------------------
#>
#> Formula: Y ~ Z
#>
#> A single draw of the estimator:
#> estimator term estimate std.error statistic p.value conf.low conf.high
#> estimator Z 0.187191 0.07652242 2.446224 0.01530916 0.03628743 0.3380945
#> df outcome inquiry
#> 198 Y ATE
#>
design <-
declare_model(
N = 500,
noise = rnorm(N),
Y_Z_0 = noise,
Y_Z_1 = noise + rnorm(N, mean = 2, sd = 2)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N, n = 250)) +
declare_assignment(Z = complete_ra(N, m = 25)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
summary(design)
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 500, noise = rnorm(N), Y_Z_0 = noise, Y_Z_1 = noise + rnorm(N, mean = 2, sd = 2))
#>
#> N = 500
#>
#> Added variable: ID
#> N_missing N_unique class
#> 0 500 character
#>
#> Added variable: noise
#> min median mean max sd N_missing N_unique
#> -3.85 -0.11 -0.04 3.08 0.98 0 500
#>
#> Added variable: Y_Z_0
#> min median mean max sd N_missing N_unique
#> -3.85 -0.11 -0.04 3.08 0.98 0 500
#>
#> Added variable: Y_Z_1
#> min median mean max sd N_missing N_unique
#> -4.26 1.93 2 7.81 2.21 0 500
#>
#> Step 2 (inquiry): declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) -------------------
#>
#> A single draw of the inquiry:
#> inquiry estimand
#> ATE 2.040887
#>
#> Step 3 (sampling): declare_sampling(S = complete_rs(N, n = 250)) ---------------
#>
#> N = 250 (250 subtracted)
#>
#> Added variable: S
#> 1
#> 250
#> 1.00
#>
#> Altered variable: ID
#> Before:
#> N_missing N_unique class
#> 0 500 character
#>
#> After:
#> N_missing N_unique class
#> 0 250 character
#>
#> Altered variable: noise
#> Before:
#> min median mean max sd N_missing N_unique
#> -3.85 -0.11 -0.04 3.08 0.98 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -2.43 0.03 -0.01 2.79 0.98 0 250
#>
#> Altered variable: Y_Z_0
#> Before:
#> min median mean max sd N_missing N_unique
#> -3.85 -0.11 -0.04 3.08 0.98 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -2.43 0.03 -0.01 2.79 0.98 0 250
#>
#> Altered variable: Y_Z_1
#> Before:
#> min median mean max sd N_missing N_unique
#> -4.26 1.93 2 7.81 2.21 0 500
#>
#> After:
#> min median mean max sd N_missing N_unique
#> -2.87 1.98 2.09 7.81 2.18 0 250
#>
#> Step 4 (assignment): declare_assignment(Z = complete_ra(N, m = 25)) ------------
#>
#> Added variable: Z
#> 0 1
#> 225 25
#> 0.90 0.10
#>
#> Step 5 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Added variable: Y
#> min median mean max sd N_missing N_unique
#> -2.46 0.16 0.27 7.81 1.41 0 250
#>
#> Step 6 (estimator): declare_estimator(Y ~ Z, inquiry = "ATE") ------------------
#>
#> Formula: Y ~ Z
#>
#> A single draw of the estimator:
#> estimator term estimate std.error statistic p.value conf.low conf.high
#> estimator Z 2.829667 0.420559 6.728346 1.179393e-10 2.001344 3.657989
#> df outcome inquiry
#> 248 Y ATE
#>