DTT: IPW estimator for the Distributional Treatment Effect on the...

In pedrohcgs/IPS: IPS: Covariate Distribution Balance via Integrated Propensity Scores

Description

IPW estimator for the Distributional Treatment Effect on the Treated

Usage

DTT(
  y,
  d,
  x,
  ps,
  beta.lin.rep,
  ysup = NULL,
  trim = FALSE,
  trim.at = NULL,
  whs = NULL
)

Arguments

y	An n x 1 vector of outcome of interest.
d	An n x 1 vector of binary treatment adoption indicators.
x	An n x k matrix of covariates used in the propensity score estimation
ps	An n x 1 vector of fitted propensity scores.
beta.lin.rep	An n x k matrix of estimates of the asymptotic linear representaion of the propensity score parameters (used to compute std. errors).
ysup	An l x 1 vector of points in the support of y to compute the DTT at. If NULL, then we set ysup to be all unique points in the support of y.
trim	Logical argument to whether one should trim propensity scores. Deafault is FALSE.
trim.at	Only used if trim=TRUE. If a scalar, trim all propensity score below trim.at and above 1 - trim.at. If a 2 x 1 vector, trim all propensity scores below trim.at[1] and all propensity scores above trim.at[2]. If NULL, trim.at is set to 1e-10.
whs	An optional n x 1 vector of weights to be used. If NULL, then every observation has the same weights.

Value

A list containing the following components:

dtt	The estimated DTT
dtt.se	Estimated (pointwise) std. error of the DTT.
dtt.inf	Estimated influence function of DTT estimator.
ysup	The evaluation points of DTT.

References

Sant'Anna, Pedro H. C, Song, Xiaojun, and Xu, Qi (2019), Covariate Distribution Balance via Propensity Scores, Working Paper <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3258551>.

pedrohcgs/IPS documentation built on Dec. 22, 2021, 7:39 a.m.