rbwPoint: Residual Balancing Weights for Estimating the Average...

In rbw: Residual Balancing Weights for Marginal Structural Models

Residual Balancing Weights for Estimating the Average Treatment Effect (ATE) in a Point Treatment Setting

Description

rbwPoint is a function that produces residual balancing weights in a point treatment setting. It takes a set of baseline confounders and computes the residuals for each confounder by centering it around its sample mean. The weights can be used to fit marginal structural models to estimate the average treatment effect (ATE).

Usage

rbwPoint(
  treatment,
  data,
  baseline_x,
  base_weights,
  max_iter = 200,
  tol = 1e-04,
  print_level = 1
)

Arguments

treatment	A symbol or character string for the treatment variable in data.
data	A data frame containing all variables in the model.
baseline_x	An expression for a set of baseline confounders stored in data or a character vector of the names of these variables.
base_weights	(Optional) A vector of base weights (or its name).
max_iter	Maximum number of iterations for Newton's method in entropy minimization.
tol	Tolerance parameter used to determine convergence in entropy minimization. See documentation for eb2.
print_level	The level of printing. See documentation for eb2.

Value

A list containing the results.

weights	A vector of residual balancing weights.
constraints	A matrix of (linearly independent) residual balancing constraints
eb_out	Results from calling the eb2 function
call	The matched call.

Examples

# residual balancing weights
rbwPoint_fit <- rbwPoint(treat, baseline_x = c(log_TotalPop, PercentOver65, log_Inc,
  PercentHispanic, PercentBlack, density,
  per_collegegrads, CanCommute), data = advertisement)

# attach residual balancing weights to data
advertisement$rbw_point <- rbwPoint_fit$weights

# fit marginal structural model
if(require(survey)){
  rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw_point, data = advertisement)
  # the outcome model includes the treatment, the square of the treatment,
  # and state-level fixed effects (Fong, Hazlett, and Imai 2018)
  msm_rbwPoint <- svyglm(Cont ~ treat + I(treat^2) + factor(StFIPS), design = rbw_design)
  summary(msm_rbwPoint)
}

rbw documentation built on March 18, 2022, 5:35 p.m.