Compute subclassification ATT and ATE
subclassify.RdCompute subclassification ATT and ATE
Arguments
- df
data.table
- x
character vector of discrete covariates
- y
outcome
- w
treatment
- debug
boolean to return strata-specific estimates
Examples
data(lalonde.psid); setDT(lalonde.psid)
subclassify(lalonde.psid,
x = c("u74", "u75"),
y = "re78", w = "treat"
)
#> $est
#> ATE ATT
#> 1 -15157.89 -4286.105
#>
#> $table
#> Key: <u74, u75>
#> u74 u75 grpmean_0 grpmean_1 N_0 N_1 DiM N_k
#> <num> <num> <num> <num> <int> <int> <num> <int>
#> 1: 0 0 23366.499 5487.466 2188 52 -17879.032 2240
#> 2: 0 1 11523.244 4440.835 87 2 -7082.409 89
#> 3: 1 0 22497.160 10514.686 53 22 -11982.474 75
#> 4: 1 1 2151.187 5954.485 162 109 3803.298 271
#>
subclassify(lalonde.psid,
x = c("married", "black", "hispanic", "u74", "u75"),
y = "re78", w = "treat"
)
#> $est
#> ATE ATT
#> 1 NA NA
#>
#> $table
#> Key: <married, black, hispanic, u74, u75>
#> married black hispanic u74 u75 grpmean_0 grpmean_1 N_0 N_1
#> <num> <num> <num> <num> <num> <num> <num> <int> <int>
#> 1: 0 0 0 0 0 21328.9403 9823.470 170 3
#> 2: 0 0 0 0 1 13616.4292 NA 9 NA
#> 3: 0 0 0 1 0 14161.5464 9900.017 6 3
#> 4: 0 0 0 1 1 901.4132 7017.448 10 9
#> 5: 0 0 1 0 0 11821.8125 2709.584 3 3
#> 6: 0 0 1 0 1 8275.2686 NA 1 NA
#> 7: 0 0 1 1 0 3657.3733 5112.010 1 1
#> 8: 0 0 1 1 1 0.0000 11359.417 1 4
#> 9: 0 1 0 0 0 13003.9556 4206.355 116 33
#> 10: 0 1 0 0 1 4786.3565 8881.670 5 1
#> 11: 0 1 0 1 0 3960.3071 12512.077 2 12
#> 12: 0 1 0 1 1 984.8227 5236.514 9 81
#> 13: 1 0 0 0 0 26213.5951 0.000 1387 1
#> 14: 1 0 0 0 1 10265.8871 NA 49 NA
#> 15: 1 0 0 1 0 25904.1864 4232.310 41 1
#> 16: 1 0 0 1 1 2585.7861 12418.100 113 1
#> 17: 1 0 1 0 0 23948.4061 8440.630 64 2
#> 18: 1 0 1 0 1 29923.9629 NA 4 NA
#> 19: 1 0 1 1 1 1266.6228 2787.960 7 1
#> 20: 1 1 0 0 0 18002.4567 9205.812 448 10
#> 21: 1 1 0 0 1 11844.3673 0.000 19 1
#> 22: 1 1 0 1 0 11243.5292 8426.758 3 5
#> 23: 1 1 0 1 1 1343.3878 7775.420 22 13
#> married black hispanic u74 u75 grpmean_0 grpmean_1 N_0 N_1
#> DiM N_k
#> <num> <int>
#> 1: -11505.470 173
#> 2: NA NA
#> 3: -4261.530 9
#> 4: 6116.035 19
#> 5: -9112.228 6
#> 6: NA NA
#> 7: 1454.637 2
#> 8: 11359.417 5
#> 9: -8797.601 149
#> 10: 4095.314 6
#> 11: 8551.770 14
#> 12: 4251.692 90
#> 13: -26213.595 1388
#> 14: NA NA
#> 15: -21671.876 42
#> 16: 9832.314 114
#> 17: -15507.776 66
#> 18: NA NA
#> 19: 1521.337 8
#> 20: -8796.645 458
#> 21: -11844.367 20
#> 22: -2816.771 8
#> 23: 6432.032 35
#> DiM N_k
#>