This paper presents an overview of the efficient Monte Carlo counterparty credit risk (CCR) estimation framework recently developed by Ghamami and Zhang (2014). We focus on the estimation of credit value adjustment (CVA), one of the most widely used and regulatory-driven counterparty credit risk measures. Our proposed efficient CVA estimators are developed based on novel applications of well-known mean square error (MSE) reduction techniques in the simulation literature. Our numerical examples illustrate that the efficient estimators outperform the existing crude estimators of CVA substantially in terms of MSE.