summarize_sensitivity: Sensitivity Summary

In senseweight: Sensitivity Analysis for Weighted Estimators

Sensitivity Summary

Description

Returns a data.frame or Kable table with summary measures of sensitivity

Usage

summarize_sensitivity(
  weights = NULL,
  Y = NULL,
  Z = NULL,
  b_star = 0,
  estimate = NULL,
  SE = NULL,
  unweighted = NULL,
  sigma2 = NULL,
  estimand = "ATT",
  pretty = FALSE,
  svy_srs = NULL,
  svy_wt = NULL,
  sig.fig = 2
)

Arguments

weights	Vector of estimated weights
Y	Outcome of interest
Z	Treatment assignment (Not needed for settings when users are analyzing surveys)
b_star	Killer confounder threshold. If not specified, will be automatically set to 0.
estimate	(Optional) Weighted point estimate. If not specified, function will automatically generate the weighted estimator, given the inputs in Y and weights
SE	(Optional) Standard error associated with the weighted point estimate
unweighted	(Optional) Unweighted point estimate.
sigma2	(Optional) Variance of outcomes or individual-level treatment effect in PATE case. In the case of a PATE estimator, if not specified, function will automatically estimate an upper bound for the variance of the individual-level treatment effect.
estimand	Specifies estimand; possible parameters include "ATT", "PATE", or "Survey"
pretty	If set to TRUE, will return a Kable table. If set to FALSE, will return a data.frame.
svy_srs	Unweighted svymean object
svy_wt	Weighted svymean object
sig.fig	Significant figures to round the output to (default set to 2)

Value

Sensitivity summary

Examples

data(jtpa_women)
site_name <- "NE"
df_site <- jtpa_women[which(jtpa_women$site == site_name), ]
df_else <- jtpa_women[which(jtpa_women$site != site_name), ]

# Estimate unweighted estimator:
model_dim <- estimatr::lm_robust(Y ~ T, data = df_site)
PATE <- coef(lm(Y ~ T, data = df_else))[2]
DiM <- coef(model_dim)[2]
# Generate weights using observed covariates:
df_all <- jtpa_women
df_all$S <- ifelse(jtpa_women$site == "NE", 1, 0)
model_ps <- WeightIt::weightit(
  (1 - S) ~ . - site - T - Y, 
  data = df_all, method = "ebal", estimand = "ATT"
)
weights <- model_ps$weights[df_all$S == 1]
# Estimate IPW model:
model_ipw <- estimatr::lm_robust(Y ~ T, data = df_site, weights = weights)
ipw <- coef(model_ipw)[2]
# Estimate bound for var(tau):
vartau <- var(df_site$Y[df_site$T == 1]) - var(df_site$Y[df_site$T == 0])
summarize_sensitivity(weights = weights, 
Y = df_site$Y, 
Z = df_site$T, 
sigma2 = vartau,
 estimand = "PATE")

senseweight documentation built on Aug. 23, 2025, 1:11 a.m.