vcov_outcome.CBPSContinuous

object

The covariates (including the treatment and an intercept term) that predict the outcome.

The coefficients from regressing Y on Z, weighting by the cbpsfit$weights.

delta

Tolerance for choosing whether to improve conditioning of the "M"
matrix prior to conversion. Equal to 1/(condition number), i.e. the
smallest eigenvalue divided by the largest.

The amount to be added to the diagonal of M if the condition of the matrix is worse than tol.

lambda

Implements the covariate balancing propensity score (CBPS) proposed
by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
estimated such that it maximizes the resulting covariate balance as well as the
prediction of treatment assignment. The method, therefore, avoids an iteration
between model fitting and balance checking. The package also implements optimal
CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,
several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
They include the CBPS for longitudinal settings so that it can be used in
conjunction with marginal structural models from Imai and Ratkovic (2015)
<DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment
variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018)
<DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of
covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation
with multiple distinct binary treatments administered simultaneously. In the future
it will be extended to other settings including the generalization of experimental
and instrumental variable estimates.

vcov_outcome.CBPSContinuous: vcov_outcome

Description

Usage

Arguments

Value