hetcv.test | R Documentation |
This function calculates statistics related to the test of heterogeneous treatment effects across groups under cross-validation.
hetcv.test(T, tau, Y, ind, ngates = 5)
T |
A vector of the unit-level binary treatment receipt variable for each sample. |
tau |
A vector of the unit-level continuous score. Conditional Average Treatment Effect is one possible measure. |
Y |
A vector of the outcome variable of interest for each sample. |
ind |
A vector of integers (between 1 and number of folds inclusive) indicating which testing set does each sample belong to. |
ngates |
The number of groups to separate the data into. The groups are determined by |
The details of the methods for this design are given in Imai and Li (2022).
A list that contains the following items:
stat |
The estimated statistic for the test of heterogeneity under cross-validation. |
pval |
The p-value of the null hypothesis (that the treatment effects are homogeneous) |
Michael Lingzhi Li, Technology and Operations Management, Harvard Business School mili@hbs.edu, https://www.michaellz.com/;
Imai and Li (2022). “Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments”,
T = c(1,0,1,0,1,0,1,0)
tau = matrix(c(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9),nrow = 8, ncol = 2)
Y = c(4,5,0,2,4,1,-4,3)
ind = c(rep(1,4),rep(2,4))
hettestlist <- hetcv.test(T,tau,Y,ind,ngates=2)
hettestlist$stat
hettestlist$pval
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