IPTW GLM, Inverse Probaibilty of Treatment Weighted GLM

Fits GLM model with treatment weights $$ w(A)= \sum_a I(A=a)/P(A=a|X) $$, computes standard errors via influence functions that are returned as the IID argument. Propensity scores are fitted using either logistic regression (glm) or the multinomial model (mlogit) when more than two categories for treatment. The treatment needs to be a factor and is identified on the rhs of the "treat.model".

glm_IPTW(
  formula,
  data,
  treat.model = NULL,
  family = binomial(),
  id = NULL,
  weights = NULL,
  estpr = 1,
  pi0 = 0.5,
  ...
)

Arguments

formula

for glm

data

data frame for risk averaging

treat.model

propensity score model (binary or multinomial)

family

of glm (logistic regression)

id

cluster id for standard errors

weights

may be given, and then uses weights*w(A) as the weights

estpr

to estimate propensity scores and get infuence function contribution to uncertainty

pi0

fixed simple weights

...

arguments for glm call

Details

Also works with cluster argument.

Author

Thomas Scheike