An example of inverse propensity weighting and weighted least squares regression adjustment for estimating an average treatment effect among treated units, using the entropy-balancing propensity score

ebal-IPW-WLS.R

library(cobalt)
library(WeightIt)
library(marginaleffects)

data("lalonde")
lalonde_trt <- subset(lalonde, treat == 1)

# ATT weighting with entropy-balancing

w_ebal <- weightit(
  treat ~ age + educ + race + married + nodegree + re74 + re75, 
  data = lalonde, 
  method = "ebal",
  estimand = "ATT"
)

IPW_fix <- 
  lm_weightit(
    re78 ~ treat, 
    data = lalonde,
    weightit = w_ebal, 
    vcov = "HC0"
  ) |> 
  avg_comparisons(variables = "treat", newdata = lalonde_trt)

IPW_inf <- 
  lm_weightit(
    re78 ~ treat, 
    data = lalonde,
    weightit = w_ebal
  ) |>
  avg_comparisons(variables = "treat", newdata = lalonde_trt)
IPW_inf

WLS_fix <- 
  lm_weightit(
    re78 ~ treat * (age + educ + race + married + nodegree + re74 + re75), 
    data = lalonde,
    weightit = w_ebal, 
    vcov = "HC0"
  ) |>
  avg_comparisons(variables = "treat", newdata = lalonde_trt)
WLS_fix

WLS_inf <- 
  lm_weightit(
    re78 ~ treat * (age + educ + race + married + nodegree + re74 + re75), 
    data = lalonde,
    weightit = w_ebal
  ) |> 
  avg_comparisons(variables = "treat", newdata = lalonde_trt)
WLS_inf