cal_rv: Calculate Robustness Value When Executing Worstcase...

In CopSens: Copula-Based Sensitivity Analysis for Observational Causal Inference

Calculate Robustness Value When Executing Worstcase Calibration

Description

Calculate Robustness Value When Executing Worstcase Calibration

Usage

cal_rv(
  y,
  tr,
  t1,
  t2,
  mu_y_dt = NULL,
  sigma_y_t = NULL,
  mu_u_dt = NULL,
  cov_u_t = NULL,
  nU = NULL,
  ...
)

Arguments

y	data.frame, matrix or vector. Gaussian outcome variable.
tr	data.frame. Treatment variables with rows corresponding to observations and columns to variables.
t1	data.frame. First treatment arms of interest. May contain a single or multiple treatments in rows.
t2	data.frame. Second treatment arms of interest, which has same number of row as t1.
mu_y_dt	an optional scalar or vector that contains naive estimates of treatment effects ignoring confounding.
sigma_y_t	an optional scalar of the standard deviation of outcome conditional on treatments.
mu_u_dt	an optional matrix of difference in conditional confounder means, E(U \mid t1) - E(U \mid t2), with latent variables in columns.
cov_u_t	an optional covariance matrix of confounders conditional on treatments.
nU	Number of latent confounders to consider.
...	further arguments passed to kEstimate, pca

Value

A numeric vector with elements being the robustness value or NA if the ignorance region doesn't contains 0 for each contrast of interest.

Examples

# load the example data #
y <- GaussianT_GaussianY$y
tr <- subset(GaussianT_GaussianY, select = -c(y))
# calculate robustness value #
cal_rv(y = y, tr = tr, t1 = tr[1:2,], t2 = tr[3:4,])

CopSens documentation built on May 12, 2022, 9:06 a.m.