Estimate the ATE of the mean difference in multiple bi-level subgroups defined by the proportions

If only care about the higher subgroup (above cutoff), only need trt.est.high so set onlyhigh to be TRUE. Scores are adjusted to the opposite sign if higher.y == FALSE; scores stay the same if higher.y == TRUE. This is because estcount.bilevel.subgroups() always takes the subgroup of the top highest adjusted scores,and higher adjusted scores should always represent high responders in treatment group 1.

Usage

estmean.bilevel.subgroups(
  y,
  x.cate,
  x.ps,
  trt,
  score,
  higher.y,
  prop,
  onlyhigh,
  ps.method = "glm",
  minPS = 0.01,
  maxPS = 0.99
)

Arguments

y: Observed outcome; vector of size n (observations)
x.cate: Matrix of p.cate baseline covariates; dimension n by p.cate (covariates in the outcome model)
x.ps: Matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept); dimension n by p.ps + 1 (covariates in the propensity score model plus intercept)
trt: Treatment received; vector of size n units with treatment coded as 0/1
score: Estimated CATE scores for all n observations from one of the six methods (boosting, linear regression, two regressions, contrast regression, random forest, generalized additive model); vector of size n
higher.y: A logical value indicating whether higher (TRUE) or lower (FALSE) values of the outcome are more desirable. Default is TRUE.
prop: Proportions corresponding to percentiles in the estimated CATE scores that define subgroups to calculate ATE for; vector of floats in `(0, 1]` (if onlyhigh=T) or in `(0, 1)` (if onlyhigh=F): Each element of prop represents the high/low cutoff in each bi-level subgroup and the length of prop is number of bi-level subgroups
onlyhigh: Indicator of returning only the ATEs in the higher-than-cutoff category of the bi-level subgroups; boolean
ps.method: A character value for the method to estimate the propensity score. Allowed values include one of: 'glm' for logistic regression with main effects only (default), or 'lasso' for a logistic regression with main effects and LASSO penalization on two-way interactions (added to the model if interactions are not specified in ps.model). Relevant only when ps.model has more than one variable.
minPS: A numerical value (in `[0, 1]`) below which estimated propensity scores should be truncated. Default is 0.01.
maxPS: A numerical value (in `(0, 1]`) above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.

Value

ate.est.high: estimated ATEs in the multiple bi-level subgroups that are in the higher-than-cutoff category; vector of size equal to the length of prop; always returned ate.est.low: estimated ATEs in the multiple bi-level subgroups that are in the lower-than-cutoff category; vector of size equal to the length of prop; returned only when onlyhigh == TRUE