attcpo
attcpo.Rdcompute the Average Treatment Effect on the Treated Conditional on the previous outcome (ATT-CPO)
attcpo(
formla,
t,
tmin1,
tmin2,
tname,
data,
idname,
Y0tqteobj,
h = NULL,
yseq = NULL,
yseqlen = 100,
se = TRUE,
iters = 100,
method = "level"
)Arguments
- formla
e.g. y ~ treat
- t
the last time period
- tmin1
the middle time period
- tmin2
the first time period
- tname
the name of the column containing time periods in the data
- data
a data.frame
- idname
the name of the column containing an individual identifier over time
- Y0tqteobj
a qte object (from the qte package) containing the the counterfactual distribution of untreated potential outcomes for the treated group
- h
optional bandwidth
- yseq
optional sequence of y values, default is to use all unique yvalues in the data, though this can increase computation time
- yseqlen
optional length of y values to use, aids in automatically generating yseq if desired
- se
whether or not to compute standard errors
- iters
how many bootstrap iterations to use if computing standard errors; default is 100.
- method
should be either "levels" or "rank"; whether to compute the ATT-CPO using based on the levels of Y0tmin1 or the ranks of Y0tmin1; "levels" is the default.
Value
att-cpo
Examples
data(displacements)
cc <- qte::CiC(learn ~ treat,
t=2011, tmin1=2007, tname="year",
idname="id", panel=TRUE, data=displacements,
probs=seq(.05,.95,.01),se=FALSE)
#> Warning: dropping 266 observations that are not in period: 2011, 2007, ...
#> Warning: covariates appear to vary over time...
#> only conditioning on first period covariates...
#> this is recommended practice, but worth noting...
cc$F.treated.tmin1 <- ecdf(subset(displacements, year==2007 & treat==1)$learn)
cc$F.treated.tmin2 <- ecdf(subset(displacements, year==2003 & treat==1)$learn)
ac <- attcpo(learn ~ treat, 2011, 2007, 2003, "year", displacements,
"id", cc, method="rank", yseqlen=10)
#> NULL
ac
#> $y.seq
#> [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
#>
#> $attcpo
#> [1] -0.6139816 -0.5746135 -0.4926910 -0.4654546 -0.4389082 -0.4052153
#> [7] -0.3382524 -0.2784216 -0.3482870 -0.4197103
#>
#> $ey1
#> [1] 9.926265 10.119976 10.215522 10.179855 10.275066 10.353360 10.299730
#> [8] 10.332749 10.365414 10.394630
#>
#> $ey0
#> [1] 10.54025 10.69459 10.70821 10.64531 10.71397 10.75858 10.63798 10.61117
#> [9] 10.71370 10.81434
#>
#> $attcpo.se
#> [1] 0.2434369 0.1783067 0.1521552 0.1391474 0.1617171 0.1723650 0.1799800
#> [8] 0.1820028 0.2074643 0.3977690
#>
#> $V
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.059261547 0.0271346702 7.922798e-03 -0.0049010583 -1.065913e-02
#> [2,] 0.027134670 0.0317932834 1.922095e-02 0.0005911454 -7.678945e-03
#> [3,] 0.007922798 0.0192209542 2.315120e-02 0.0121502120 6.131054e-05
#> [4,] -0.004901058 0.0005911454 1.215021e-02 0.0193619973 1.446165e-02
#> [5,] -0.010659128 -0.0076789447 6.131054e-05 0.0144616515 2.615243e-02
#> [6,] -0.010391781 -0.0090110230 -5.079483e-03 0.0036000703 1.963714e-02
#> [7,] -0.012595921 -0.0078279414 -5.928427e-03 -0.0036692683 5.973020e-03
#> [8,] -0.013583056 -0.0054590911 -5.195060e-03 -0.0059037012 -2.066107e-04
#> [9,] -0.009568156 -0.0032187790 -4.423962e-03 -0.0051206503 -2.336972e-03
#> [10,] -0.007885109 -0.0008383711 -4.322484e-03 -0.0060123482 -5.358734e-03
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] -0.010391781 -0.012595921 -0.0135830560 -0.009568156 -0.0078851087
#> [2,] -0.009011023 -0.007827941 -0.0054590911 -0.003218779 -0.0008383711
#> [3,] -0.005079483 -0.005928427 -0.0051950600 -0.004423962 -0.0043224842
#> [4,] 0.003600070 -0.003669268 -0.0059037012 -0.005120650 -0.0060123482
#> [5,] 0.019637138 0.005973020 -0.0002066107 -0.002336972 -0.0053587341
#> [6,] 0.029709706 0.023154448 0.0110986983 0.001456219 -0.0046638941
#> [7,] 0.023154448 0.032392816 0.0247329547 0.009074171 -0.0048874575
#> [8,] 0.011098698 0.024732955 0.0331250224 0.025474571 0.0007723600
#> [9,] 0.001456219 0.009074171 0.0254745711 0.043041430 0.0434937385
#> [10,] -0.004663894 -0.004887458 0.0007723600 0.043493739 0.1582201895
#>
#> $Y0tqteobj
#> $qte
#> 5% 6% 7% 8% 9% 10% 11%
#> -1.5686159 -1.4552872 -1.0986123 -1.0986123 -1.1526795 -1.0498221 -1.0216512
#> 12% 13% 14% 15% 16% 17% 18%
#> -0.8556661 -0.7884574 -0.6931472 -0.6998331 -0.7339692 -0.7339692 -0.7308075
#> 19% 20% 21% 22% 23% 24% 25%
#> -0.6190392 -0.6380874 -0.5690945 -0.5596158 -0.4989912 -0.4989912 -0.5108256
#> 26% 27% 28% 29% 30% 31% 32%
#> -0.4054651 -0.4700036 -0.5596158 -0.5108256 -0.4643056 -0.4198538 -0.4198538
#> 33% 34% 35% 36% 37% 38% 39%
#> -0.4198538 -0.3920421 -0.4700036 -0.4426998 -0.3566749 -0.3566749 -0.3566749
#> 40% 41% 42% 43% 44% 45% 46%
#> -0.3364722 -0.3364722 -0.3364722 -0.3166696 -0.3036824 -0.2876821 -0.3184537
#> 47% 48% 49% 50% 51% 52% 53%
#> -0.3184537 -0.3184537 -0.2876821 -0.2578291 -0.2547750 -0.2513144 -0.2513144
#> 54% 55% 56% 57% 58% 59% 60%
#> -0.2513144 -0.2231436 -0.2772108 -0.3934737 -0.3184537 -0.3184537 -0.3184537
#> 61% 62% 63% 64% 65% 66% 67%
#> -0.3184537 -0.3184537 -0.3184537 -0.2937611 -0.2696636 -0.2696636 -0.2588616
#> 68% 69% 70% 71% 72% 73% 74%
#> -0.2363888 -0.2363888 -0.2363888 -0.2363888 -0.2537805 -0.1788792 -0.1484200
#> 75% 76% 77% 78% 79% 80% 81%
#> -0.1484200 -0.1240526 -0.1035407 -0.1358015 -0.1685725 -0.1172792 -0.1780067
#> 82% 83% 84% 85% 86% 87% 88%
#> -0.1743534 -0.1664482 -0.1586050 -0.1431008 -0.1431008 -0.2017687 -0.1808412
#> 89% 90% 91% 92% 93% 94% 95%
#> -0.1941560 -0.1941560 -0.1177830 -0.1471576 -0.1423162 -0.1823216 -0.1512310
#>
#> $ate
#> [1] -0.4458143
#>
#> $qte.se
#> NULL
#>
#> $qte.lower
#> NULL
#>
#> $qte.upper
#> NULL
#>
#> $ate.se
#> NULL
#>
#> $ate.lower
#> NULL
#>
#> $ate.upper
#> NULL
#>
#> $c
#> NULL
#>
#> $pscore.reg
#> NULL
#>
#> $probs
#> [1] 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19
#> [16] 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.30 0.31 0.32 0.33 0.34
#> [31] 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49
#> [46] 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.60 0.61 0.62 0.63 0.64
#> [61] 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79
#> [76] 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.90 0.91 0.92 0.93 0.94
#> [91] 0.95
#>
#> $type
#> [1] "On the Treated"
#>
#> $F.treated.t
#> Empirical CDF
#> Call: stats::ecdf(treated.t[, yname])
#> x[1:80] = 7.0031, 7.2442, 7.6009, ..., 12.044, 12.748
#>
#> $F.untreated.t
#> Empirical CDF
#> Call: stats::ecdf(untreated.t[, yname])
#> x[1:86] = 8.5172, 8.9872, 9.2103, ..., 12.09, 12.748
#>
#> $F.treated.t.cf
#> Empirical CDF
#> Call: ecdf(quantile(untreated.t[, yname], probs = F.untreated.tmin1(treated.tmin1[,
#> yname]), type = 1))
#> x[1:42] = 8.5172, 9.2103, 9.3927, ..., 12.044, 12.748
#>
#> $F.treated.tmin1
#> Empirical CDF
#> Call: ecdf(subset(displacements, year == 2007 & treat == 1)$learn)
#> x[1:78] = 8.6482, 9.2103, 9.6158, ..., 11.849, 12.637
#>
#> $F.treated.tmin2
#> Empirical CDF
#> Call: ecdf(subset(displacements, year == 2003 & treat == 1)$learn)
#> x[1:74] = 8.2266, 8.8537, 8.9872, ..., 11.791, 12.491
#>
#> $F.treated.change.tmin1
#> NULL
#>
#> $F.untreated.change.t
#> NULL
#>
#> $F.untreated.change.tmin1
#> NULL
#>
#> $F.untreated.tmin1
#> Empirical CDF
#> Call: stats::ecdf(untreated.tmin1[, yname])
#> x[1:79] = 7.6962, 9.6158, 9.6803, ..., 11.938, 12.637
#>
#> $F.untreated.tmin2
#> NULL
#>
#> $condQ.treated.t
#> NULL
#>
#> $condQ.treated.t.cf
#> NULL
#>
#> $eachIterList
#> NULL
#>
#> $inffunct
#> NULL
#>
#> $inffuncu
#> NULL
#>
#> attr(,"class")
#> [1] "QTE"
#>
#> attr(,"class")
#> [1] "ATTCPO.OBJ"
ggattcpo(ac)
