Native rmorie implementation of Coarsened Exact Matching (Iacus, King
& Porro 2012): numeric covariates are coarsened into bins (quantile
cutpoints; Sturges' rule when n_bins is NA for a
variable), units are exact-matched on the coarsened strata, and
controls receive CEM stratum weights (weights and
subclass columns on the matched data). The multivariate L1
imbalance of the stratification is reported in
details$l1_before. No MatchIt/cem at runtime.
References
Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1–24.
Examples
# \donttest{
set.seed(1)
df <- data.frame(y = rnorm(200), d = rbinom(200, 1, 0.4),
x1 = rnorm(200), x2 = rnorm(200))
morie_matching_cem(df, "d", c("x1", "x2"), n_bins = 5)
#> $matched_data
#> y d x1 x2 weights subclass
#> 1 -0.626453811 1 0.893673702 0.077303123 1.0000000 11
#> 2 0.183643324 0 -1.047298149 -0.296868642 0.2436782 20
#> 3 -0.835628612 1 1.971337386 -1.183242240 1.0000000 19
#> 4 1.595280802 1 -0.383632106 0.011292688 1.0000000 6
#> 5 0.329507772 1 1.654145302 0.991601036 1.0000000 18
#> 6 -0.820468384 1 1.512212694 1.593967454 1.0000000 18
#> 7 0.487429052 0 0.082965734 -1.372711271 0.4873563 5
#> 8 0.738324705 1 0.567220915 -0.249610933 1.0000000 11
#> 9 0.575781352 0 -1.024548480 1.159424527 0.5415070 23
#> 10 -0.305388387 1 0.323006503 -1.114222348 1.0000000 15
#> 11 1.511781168 1 1.043612458 -2.528500689 1.0000000 19
#> 12 0.389843236 0 0.099078487 -0.935902559 0.3655172 2
#> 13 -0.621240581 1 -0.454136909 -0.967239458 1.0000000 7
#> 14 -2.214699887 1 -0.655781852 0.047488592 1.0000000 6
#> 15 1.124930918 1 -0.035922423 -0.403736793 1.0000000 2
#> 16 -0.044933609 0 1.069161461 0.231496128 1.2183908 17
#> 17 -0.016190263 0 -0.483974930 -0.422372408 0.1740558 7
#> 18 0.943836211 0 -0.121010111 0.374118395 1.2183908 3
#> 19 0.821221195 1 -1.294140004 -0.366005775 1.0000000 21
#> 20 0.593901321 1 0.494312836 1.190101447 1.0000000 14
#> 22 0.782136301 0 1.497041009 0.290666645 1.2183908 17
#> 23 0.074564983 1 0.814702731 -0.884849568 1.0000000 12
#> 24 -1.989351696 0 -1.869788790 0.208006479 1.5229885 22
#> 25 0.619825748 0 0.482029504 -0.047730172 1.8275862 11
#> 26 -0.056128740 0 0.456135603 -1.684520646 0.6962233 15
#> 27 -0.155795507 0 -0.353400286 -0.144226557 1.5229885 6
#> 28 -1.470752384 1 0.170489471 1.180213666 1.0000000 4
#> 29 -0.478150055 1 -0.864035954 0.681399923 1.0000000 22
#> 30 0.417941560 1 0.679230774 0.143247631 1.0000000 11
#> 31 1.358679552 1 -0.327101015 -1.192316444 1.0000000 5
#> 32 -0.102787727 0 -1.569082185 1.169228653 0.5415070 23
#> 33 0.387671612 0 -0.367450756 0.079201709 1.5229885 6
#> 35 -1.377059557 0 -0.334281365 1.642028213 1.6245211 9
#> 36 -0.414994563 1 0.732750042 -0.769592322 1.0000000 12
#> 37 -0.394289954 1 0.946585640 0.303360961 1.0000000 17
#> 38 -0.059313397 0 0.004398704 1.281737421 0.9747126 4
#> 39 1.100025372 0 -0.352322306 0.602222795 2.0306513 8
#> 40 0.763175748 1 -0.529695509 -0.307022265 1.0000000 6
#> 41 -0.164523596 0 0.739589226 -0.418418103 0.9747126 12
#> 42 -0.253361680 0 -1.063457415 0.355135530 1.5229885 22
#> 43 0.696963375 0 0.246210844 0.513481115 1.2183908 3
#> 44 0.556663199 1 -0.289499367 0.018607400 1.0000000 1
#> 45 -0.688755695 0 -2.264889356 1.318448972 0.5415070 23
#> 46 -0.707495157 0 -1.408850456 -0.065832000 0.2436782 20
#> 48 0.768532925 1 -0.191278951 0.537326132 1.0000000 3
#> 49 -0.112346212 0 0.803283216 -2.201782322 0.6962233 15
#> 50 0.881107726 0 1.887474463 0.391973744 1.2183908 17
#> 51 0.398105880 1 1.473881181 0.496960952 1.0000000 17
#> 52 -0.612026393 0 0.677268492 -0.224874715 1.8275862 11
#> 53 0.341119691 0 0.379962687 -1.117143165 0.6962233 15
#> 54 -1.129363096 0 -0.192798426 -0.394994603 0.3655172 2
#> 55 1.433023702 0 1.577891795 1.549830342 2.0306513 18
#> 56 1.980399899 1 0.596234109 -0.743514480 1.0000000 12
#> 57 -0.367221476 0 -1.173576941 -2.331712118 1.5229885 24
#> 58 -1.044134626 1 -0.155642535 0.812245442 1.0000000 4
#> 59 0.569719627 1 -1.918909820 -0.501310657 1.0000000 21
#> 60 -0.135054604 0 -0.195258846 -0.510886566 0.3655172 2
#> 61 2.401617761 0 -2.592327670 -1.215364041 1.5229885 24
#> 62 -0.039240003 0 1.314002167 -0.022558628 1.2183908 16
#> 63 0.689739362 0 -0.635543001 0.701239300 2.0306513 8
#> 64 0.028002159 0 -0.429978839 -0.587482026 0.1740558 7
#> 65 -0.743273209 0 -0.169318332 -0.606727941 0.3655172 2
#> 66 0.188792300 1 0.612218174 1.096640215 1.0000000 14
#> 67 -1.804958629 1 0.678340177 -0.247509677 1.0000000 11
#> 68 1.465554862 0 0.567951972 -0.159901713 1.8275862 11
#> 69 0.153253338 0 -0.572542604 -0.625778251 0.1740558 7
#> 70 2.172611670 1 -1.363291256 0.900434636 1.0000000 23
#> 71 0.475509529 0 -0.388722244 -0.994193629 0.1740558 7
#> 72 -0.709946431 1 0.277914132 0.849250386 1.0000000 4
#> 73 0.610726353 1 -0.823081122 0.805702289 1.0000000 9
#> 74 -0.934097632 0 -0.068840934 -0.467600936 0.3655172 2
#> 75 -1.253633400 0 -1.167662326 0.848420314 0.5415070 23
#> 76 0.291446236 0 -0.008309014 0.986769864 0.9747126 4
#> 77 -0.443291873 1 0.128855402 0.575620289 1.0000000 3
#> 78 0.001105352 0 -0.145875628 2.024842045 0.9747126 4
#> 79 0.074341324 0 -0.163910957 -1.962353191 0.4873563 5
#> 80 -0.589520946 1 1.763552003 -1.164920931 1.0000000 19
#> 81 -0.568668733 1 0.762586512 -1.376519214 1.0000000 15
#> 82 -0.135178615 0 1.111431081 0.167679934 1.2183908 17
#> 83 1.178086997 1 -0.923206953 1.584629079 1.0000000 23
#> 84 -1.523566800 1 0.164341838 1.677888953 1.0000000 4
#> 85 0.593946188 0 1.154825187 0.488296698 1.2183908 17
#> 86 0.332950371 0 -0.056521425 0.878673263 0.9747126 4
#> 87 1.063099837 0 -2.129360648 -0.144874874 0.2436782 20
#> 88 -0.304183924 0 0.344845762 0.468971760 0.9137931 13
#> 89 0.370018810 1 -1.904955446 0.376235477 1.0000000 22
#> 90 0.267098791 0 -0.811170153 -0.761040275 0.1740558 7
#> 91 -0.542520031 0 1.324004321 -0.293294934 1.2183908 16
#> 92 1.207867806 1 0.615636849 -0.134841264 1.0000000 11
#> 93 1.160402616 0 1.091668956 1.393845816 2.0306513 18
#> 94 0.700213650 0 0.306604862 -1.036988690 0.6962233 15
#> 95 1.586833455 0 -0.110158762 -2.114335148 0.4873563 5
#> 96 0.558486426 0 -0.924312773 0.768278218 0.5415070 23
#> 98 -0.573265414 0 0.045010598 -0.436106923 0.3655172 2
#> 99 -1.224612615 1 -0.715128401 0.904705031 1.0000000 9
#> 100 -0.473400636 0 0.865223100 -0.763086265 0.9747126 12
#> 101 -0.620366677 0 1.074440958 -0.341066980 1.2183908 16
#> 102 0.042115873 1 1.895654774 1.502424534 1.0000000 18
#> 103 -0.910921649 1 -0.602997304 0.528307712 1.0000000 8
#> 104 0.158028772 1 -0.390867821 0.542191355 1.0000000 8
#> 105 -0.654584644 0 -0.416222032 -0.136673356 1.5229885 6
#> 106 1.767287269 1 -0.375657423 -1.136733853 1.0000000 10
#> 107 0.716707476 0 -0.366630946 -1.496627154 0.7310345 10
#> 108 0.910174229 1 -0.295677453 -0.223385644 1.0000000 1
#> 109 0.384185358 1 1.441820410 2.001719228 1.0000000 18
#> 110 1.682176081 0 -0.697538292 0.221703816 2.0306513 8
#> 111 -0.635736454 1 -0.388167506 0.164372909 1.0000000 8
#> 112 -0.461644730 0 0.652536452 0.332623609 0.9137931 13
#> 114 -0.650696353 1 -0.772110803 -1.398754027 1.0000000 10
#> 115 -0.207380744 1 -0.508086216 2.675740796 1.0000000 9
#> 116 -0.392807929 0 0.523620590 -0.423686089 0.9747126 12
#> 117 -0.319992869 1 1.017754227 -0.298601512 1.0000000 16
#> 118 -0.279113303 0 -0.251164588 -1.792341727 0.4873563 5
#> 119 0.494188331 0 -1.429993447 -0.248008225 0.2436782 20
#> 120 -0.177330482 1 1.709121032 -0.247303918 1.0000000 16
#> 121 -0.505957462 1 1.435069572 -0.255510379 1.0000000 16
#> 122 1.343038825 1 -0.710371146 -1.786938100 1.0000000 10
#> 123 -0.214579409 0 -0.065067574 1.784662816 0.9747126 4
#> 124 -0.179556530 0 -1.759468735 1.763586348 0.5415070 23
#> 125 -0.100190741 0 0.569722972 0.689600222 0.9137931 13
#> 126 0.712666307 1 1.612346798 -1.100740644 1.0000000 19
#> 127 -0.073564404 1 -1.637280647 0.714509357 1.0000000 22
#> 128 -0.037634171 1 -0.779568513 -0.246470317 1.0000000 6
#> 129 -0.681660479 1 -0.641176934 -0.319786166 1.0000000 6
#> 130 -0.324270272 1 -0.681131394 1.362644293 1.0000000 9
#> 131 0.060160440 1 -2.033285596 -1.227882590 1.0000000 24
#> 132 -0.588894486 0 0.500963559 -0.511219233 0.9747126 12
#> 133 0.531496193 0 -1.531798140 -0.731194999 2.4367816 21
#> 134 -1.518394082 0 -0.024997639 0.019752007 0.8122605 1
#> 135 0.306557861 0 0.592984721 -1.572863915 0.6962233 15
#> 136 -1.536449824 0 -0.198195421 -0.703333270 0.3655172 2
#> 137 -0.300976127 0 0.892008392 0.715932089 2.4367816 14
#> 138 -0.528279904 0 -0.025715071 0.465214906 1.2183908 3
#> 139 -0.652094781 0 -0.647660451 -0.973902306 0.1740558 7
#> 140 -0.056896778 1 0.646359415 0.559217730 1.0000000 13
#> 141 -1.914359426 0 -0.433832740 -2.432639745 0.7310345 10
#> 142 1.176583312 1 1.772611185 -0.340484927 1.0000000 16
#> 143 -1.664972436 1 -0.018259711 0.713033195 1.0000000 3
#> 144 -0.463530401 0 0.852814994 -0.659037386 0.9747126 12
#> 145 -1.115920105 0 0.205162903 -0.036402623 0.8122605 1
#> 146 -0.750819001 0 -3.008048599 -1.593286302 1.5229885 24
#> 147 2.087166546 1 -1.366111931 0.847792797 1.0000000 23
#> 148 0.017395620 0 -0.424102260 -1.850388849 0.7310345 10
#> 149 -1.286300530 0 0.236803664 -0.323650632 0.8122605 1
#> 150 -1.640605534 1 -2.342723120 -0.255248113 1.0000000 20
#> 151 0.450187101 1 0.961696633 0.060921227 1.0000000 16
#> 152 -0.018559833 0 -0.604425734 -0.823491629 0.1740558 7
#> 153 -0.318068375 0 -0.752877279 1.829730485 1.6245211 9
#> 154 -0.929362147 1 -1.555611593 -1.429916216 1.0000000 24
#> 155 -1.487460310 1 -1.453893738 0.254137143 1.0000000 22
#> 156 -1.075192297 0 0.056331836 -2.939773695 0.4873563 5
#> 157 1.000028804 0 0.509369407 0.002415809 1.8275862 11
#> 158 -0.621266695 0 -2.097882960 0.509665571 1.5229885 22
#> 159 -1.384426847 1 -1.004361979 -1.084720001 1.0000000 24
#> 160 1.869290622 1 0.535771722 0.704832977 1.0000000 13
#> 161 0.425100377 1 -0.453037085 0.330976350 1.0000000 8
#> 162 -0.238647101 0 2.165368502 0.976327473 2.0306513 18
#> 164 0.886422651 1 0.595498034 -0.970579905 1.0000000 12
#> 165 -0.619243048 1 0.004884450 -1.771531349 1.0000000 5
#> 166 2.206102465 1 0.279360782 -0.322470342 1.0000000 11
#> 167 -0.255027030 0 -0.705906125 -1.338800742 0.7310345 10
#> 168 -1.424494650 1 0.628017153 0.688156028 1.0000000 13
#> 169 -0.144399602 0 1.480213960 0.071280652 1.2183908 16
#> 170 0.207538339 1 1.083429910 2.189752359 1.0000000 18
#> 171 2.307978399 0 -0.813244257 -1.157707599 0.7310345 10
#> 172 0.105802368 0 -1.618876849 1.181688064 0.5415070 23
#> 173 0.456998805 0 -0.109655699 -0.527368362 0.3655172 2
#> 174 -0.077152935 0 0.440889371 -1.456628011 0.6962233 15
#> 175 -0.334000842 1 1.350993980 0.572967370 1.0000000 17
#> 176 -0.034726028 0 -1.318609485 -1.433377705 1.5229885 24
#> 177 0.787639606 1 0.364384593 -1.055185019 1.0000000 15
#> 178 2.075245009 0 0.233499835 -0.733111877 0.3655172 2
#> 179 1.027392439 1 1.193955261 0.210907264 1.0000000 17
#> 180 1.207908398 1 -0.027909972 -0.998920727 1.0000000 2
#> 181 -1.231323422 0 -0.357298855 1.077850323 1.6245211 9
#> 182 0.983895570 1 -1.146814136 -1.198974383 1.0000000 24
#> 183 0.219924804 1 -0.517420484 0.216637035 1.0000000 8
#> 184 -1.467250029 0 -0.362123773 0.143087030 1.5229885 6
#> 185 0.521022743 0 2.350554326 -1.065750091 4.8735632 19
#> 187 1.464587312 0 -0.166703279 -0.656179477 0.3655172 2
#> 188 -0.766082000 0 -1.043667439 0.959394327 0.5415070 23
#> 189 -0.430211754 1 -1.972934934 1.556052636 1.0000000 23
#> 190 -0.926109497 1 0.514671633 -1.040796434 1.0000000 15
#> 191 -0.177103961 0 -1.090573584 0.930572409 0.5415070 23
#> 192 0.402011779 0 2.284659326 -0.075445931 1.2183908 16
#> 193 -0.731748173 1 -0.885617573 -1.967195349 1.0000000 24
#> 194 0.830373168 1 0.111106430 -0.755903643 1.0000000 2
#> 195 -1.208082786 1 3.810276681 0.461149161 1.0000000 17
#> 196 -1.047984413 1 -1.108909998 0.145106631 1.0000000 22
#> 197 1.441157707 0 0.307566624 -2.442311321 0.6962233 15
#> 198 -1.015847465 0 -1.106894472 0.580318685 1.5229885 22
#> 199 0.411974712 0 0.347653649 0.655051998 0.9137931 13
#> 200 -0.381076051 0 -0.873264535 -0.304508837 0.2436782 20
#>
#> $n_treated
#> [1] 87
#>
#> $n_matched_control
#> [1] 106
#>
#> $match_pairs
#> [1] treated_idx control_idx distance
#> <0 rows> (or 0-length row.names)
#>
#> $method
#> [1] "cem (rmorie native)"
#>
#> $details
#> $details$engine
#> [1] "native-cem"
#>
#> $details$n_bins
#> [1] 5
#>
#> $details$n_strata
#> [1] 24
#>
#> $details$l1_before
#> [1] 0.2843048
#>
#>
#> attr(,"class")
#> [1] "morie_match_result" "list"
# }