ovbias {bate}

Compute bias adjusted treatment effect taking parameter vector as input.

Description

Compute bias adjusted treatment effect taking parameter vector as input.

Usage

ovbias(parameters, deltalow, deltahigh, Rhigh, e)

Arguments

parameters

A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions.

deltalow

The lower limit of delta.

deltahigh

The upper limit of delta.

Rhigh

The upper limit of Rmax.

e

The step size.

Value

List with three elements:

Data

Data frame containing the bias ($bias) and bias-adjusted treatment effect ($bstar) for each point on the grid

bias_Distribution

Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of bias

bstar_Distribution

Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of the bias-adjusted treatment effect

Examples

## Load data set
data("NLSY_IQ")
 
## Set age and race as factor variables
NLSY_IQ$age <- factor(NLSY_IQ$age)
NLSY_IQ$race <- factor(NLSY_IQ$race)
   
## Collect parameters from the short, intermediate and auxiliary regressions
parameters <- collect_par(
data = NLSY_IQ, outcome = "iq_std", 
treatment = "BF_months", 
control = c("age","sex","income","motherAge","motherEDU","mom_married","race"),
other_regressors = c("sex","age"))

## Set limits for the bounded box
Rlow <- parameters$Rtilde
Rhigh <- 0.61
deltalow <- 0.01
deltahigh <- 0.99
e <- 0.01

## Not run: 
## Compute bias and bias-adjusted treatment effect
OVB <- ovbias(
parameters = parameters, 
deltalow=deltalow, 
deltahigh=deltahigh, Rhigh=Rhigh, 
e=e)

## Default quantiles of bias
(OVB$bias_Distribution)

## Chosen quantilesof bias
quantile(OVB$Data$bias, c(0.01,0.05,0.1,0.9,0.95,0.975))

## Default quantiles of bias-adjusted treatment effect
(OVB$bstar_Distribution)

## Chosen quantiles of bias-adjusted treatment effect
quantile(OVB$Data$bstar, c(0.01,0.05,0.1,0.9,0.95,0.975))

## End(Not run)