Before revision:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators  .
After revision:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators and that this improvement can be unbounded .