residualBalance.ate: Estimate ATE via approximate residual balancing

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

Description

Estimate ATE via approximate residual balancing

Usage

residualBalance.ate(X, Y, W, target.pop = c(0, 1),
  allow.negative.weights = FALSE, zeta = 0.5, fit.method = c("elnet",
  "none"), alpha = 0.9, scale.X = TRUE, estimate.se = FALSE,
  optimizer = c("mosek", "pogs", "pogs.dual", "quadprog"),
  bound.gamma = FALSE, verbose = FALSE)

Arguments

X	the input features
Y	the observed responses
W	treatment/control assignment, coded as 0/1
target.pop	which population should the treatment effect be estimated for? (0, 1): average treatment effect for everyone 0: average treatment effect for controls 1: average treatment effect for treated
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, w) = E[Y | X = x, W = w]
alpha	tuning paramter for glmnet
scale.X	whether non-binary features should be noramlized
estimate.se	whether to return estimate of standard error
optimizer	which optimizer to use for approximate balancing
bound.gamma	Whether upper bound on gamma should be imposed. This is required to guarantee asymptotic normality, but increases computational cost.
verbose	whether the optimizer should print progress information

Value

ATE estimate, along with (optional) standard error estimate

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