plotOutcomes {htetree}

Intermediate function for hte_plot_line

Description

Plots the different least squares models used to estimate heterogeneous treatment effects(HTE) at each node. At each node, this visualization aims to show how the estimated treatment effect differs when using ordinary least squares and weighted least squares methods. The weighted least squares method in this package uses inverse propensity scores as weights, in order to reduce bias due to confounding variables.

Usage

plotOutcomes(
  treatment,
  outcome,
  propscores,
  confInt = TRUE,
  colbyWt = FALSE,
  ylab = "",
  xlab = "",
  title = "",
  gamma = 0,
  lambda = 0,
  ...
)

Arguments

treatment

a character representing the column name for the treatment variable in the causal setup

outcome

a character representing the column name of the outcome variable.

propscores

a character representing the column name of the propensity score.

confInt

a logical value indicating whether adding the 95 confidence interval. The default is set as TRUE.

colbyWt

a logical value indicating whether the points are are colored according to inverse propensity scores. The default is set as FALSE.

xlab, ylab, title

Characters representing the name for x axis, y axis, and main title for each node.

gamma, lambda

numbers indicating the bias level used in sensitivity analysis

...

further arguments passed to or from other methods.

Value

A summary table after adjusting the estimates with inverse probability weighting (ipw).