hcate: Tests for Homogeneous Conditional Average Treatment Effects
In pedrohcgs/kmte: An R Package for Treatment Effects with Censored Outcomes

hcate computes Kolmogorov-Smirnov and Cramer-von Mises type tests for the null hypothesis of homogeneous conditional average treatment effects. The test is suitable for both censored and uncensored outcomes, and relies on the unconfoundedness assumption. For details of the testing procedure, see Sant'Anna (2016b),'Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes'.

1	hcate(out, delta, treat, xvector, xpscore, b, cores = 1)

`out`	vector containing the outcome of interest
`delta`	vector containing the censoring indicator (1 if observed, 0 if censored)
`treat`	vector containing the treatment indicator (1 if treated, 0 if control)
`xvector`	matrix (or data frame) containing the conditioning covariates
`xpscore`	matrix (or data frame) containing the covariates (and their transformations) to be included in the propensity score estimation
`b`	number of bootstrap draws
`cores`	number of cores to use during the bootstrap (default is 1). If cores>1, the bootstrap is conducted using parLapply, instead of lapply type call.

a list containing the Kolmogorov-Smirnov test statistic (kstest), the Cramer-von Mises test statistic (cvmtest), and their associated bootstrapped p-values, pvks and pvcvm, respectively.

pedrohcgs/kmte documentation built on May 24, 2019, 11:46 p.m.