rbwPanel {rbw}

Residual Balancing Weights for Analyzing Time-varying Treatments

Description

rbwPanel is a function that produces residual balancing weights (rbw) for estimating the marginal effects of time-varying treatments. The user supplies a long format data frame (each row being a unit-period) and a list of fitted model objects for the conditional mean of each post-treatment confounder given past treatments and past confounders. The residuals of each time-varying confounder are balanced across both the current treatment A_t and the regressors of the confounder model. In addition, when future > 0, the residuals are also balanced across future treatments A_{t+1},\ldots A_{t + future}.

Usage

rbwPanel(
  treatment,
  xmodels,
  id,
  time,
  data,
  base_weights,
  future = 1L,
  max_iter = 200,
  tol = 1e-04,
  print_level = 1
)

Arguments

treatment

A symbol or character string for the treatment variable in data.

xmodels

A list of fitted lm or glm objects for time-varying confounders.

id

A symbol or character string for the unit id variable in data.

time

A symbol or character string for the time variable in data. The time variable should be numeric.

data

A data frame containing all variables in the model.

base_weights

(Optional) A vector of base weights (or its name).

future

An integer indicating the number of future treatments in the balancing conditions. When future > 0, the residualized time-varying covariates are balanced not only with respect to current treatment A_t, but also with respect to future treatments A_{t+1},\ldots A_{t + future}.

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 data frame containing the unit id variable and 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

# models for time-varying confounders
m1 <- lm(dem.polls ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week),
data = campaign_long)
m2 <- lm(undother ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week),
data = campaign_long)

xmodels <- list(m1, m2)

# residual balancing weights
rbwPanel_fit <- rbwPanel(treatment = d.gone.neg, xmodels = xmodels, id = id,
time = week, data = campaign_long)

summary(rbwPanel_fit$weights)

# merge weights into wide-format data
campaign_wide2 <- merge(campaign_wide, rbwPanel_fit$weights, by = "id")

# fit a marginal structural model (adjusting for baseline confounders)
if(require(survey)){
  rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw, data = campaign_wide2)
  msm_rbwPanel <- svyglm(demprcnt ~ cum_neg * deminc + camp.length + factor(year) + office,
  design = rbw_design)
  summary(msm_rbwPanel)
}