residualBalance.mean: Estimate mean outcome at balance.target via residual...

In swager/balanceHD: Approximately balanced estimation of average treatment effects in high dimensions.

Description

Estimate mean outcome at balance.target via residual balancing

Usage

residualBalance.mean(XW, YW, balance.target, allow.negative.weights = FALSE,
  zeta, fit.method = c("elnet", "none"), alpha, optimizer = c("mosek",
  "pogs", "pogs.dual", "quadprog"), bound.gamma = FALSE, verbose = FALSE)

Arguments

XW	the input features for the sub-population of interest
YW	the observed responses for the sub-population of interest
balance.target	the desired center of the dataset
allow.negative.weights	whether negative gammas are allowed for balancing
zeta	tuning parameter for selecting approximately balancing weights
fit.method	the method used to fit mu(x) = E[YW | XW = x]
alpha	tuning paramter for glmnet
optimizer	which optimizer to use for approximate balancing
bound.gamma	whether upper bound on gamma should be imposed
verbose	whether the optimizer should print progress information

Value

Estimate for E[YW | XW = balance.target], along with variance estimate

swager/balanceHD documentation built on Aug. 10, 2021, 1:54 a.m.