Naive_Estimation

This function performs a naive estimation of the ATE.
This approach gives us the so-called "naive estimate" by ignoring the difference between
(X*, Y*) and (X, Y).

Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.

Aryan Rezanezhad

ATE.ERROR

Estimating ATE with Misclassified Outcomes and Mismeasured
Covariates

Grace Y. Yi

Naive_Estimation function

<dl><dt>Y_star</dt>
<dd>A numeric vector of outcomes with potential misclassification.</dd>
<dt>A</dt>
<dd>A numeric vector of treatment assignments.</dd>
<dt>Z</dt>
<dd>A numeric vector of covariate Z.</dd>
<dt>X_star</dt>
<dd>A numeric vector of covariate X with measurement error.</dd></dl>

Arguments

Naive Estimation of Average Treatment Effect — Naive_Estimation

<dl>

<dt>Y_star</dt>
<dd>A numeric vector of outcomes with potential misclassification.</dd>


<dt>A</dt>
<dd>A numeric vector of treatment assignments.</dd>


<dt>Z</dt>
<dd>A numeric vector of covariate Z.</dd>


<dt>X_star</dt>
<dd>A numeric vector of covariate X with measurement error.</dd>

</dl>

Naive_Estimation: Naive Estimation of Average Treatment Effect

Description

Usage

Value

Arguments

Examples