het.test | R Documentation |
This function calculates statistics related to the test of heterogeneous treatment effects across groups.
het.test(T, tau, Y, 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. |
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. |
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 = c(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7)
Y = c(4,5,0,2,4,1,-4,3)
hettestlist <- het.test(T,tau,Y,ngates=5)
hettestlist$stat
hettestlist$pval
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