Discussing doubly robust methods
In this section, we’ll look into advanced concepts regarding DR estimation so that we can leverage its full potential for more accurate and reliable causal inference results.
Estimating variance
The AER package, which stands for Applied Econometrics with R, is commonly used for instrumental variable estimations and other econometric models. The sandwich package, on the other hand, provides robust covariance matrix estimators, which are essential for reliable standard error estimation in the presence of heteroscedasticity or other model misspecifications.
But why do we need variance estimation? In the DR estimation method within R, variance estimation is crucial for a few key reasons.
First, accurate variance estimation allows for reliable standard error calculation, which is fundamental for hypothesis testing and constructing confidence intervals around causal effect estimates. Without proper variance estimation, we might either overstate...
