This dissertation used synthetic datasets, semi-synthetic datasets, and a real-world dataset from an educational intervention to compare the performance of 15 machine learning and multiple imputation methods to estimate the individual treatment effect (ITE). In addition, it examined the performance of five evaluation metrics that can be used to identify the best ITE estimation method when conducting research with real-world data. Among the ITE estimation methods that were analyzed, the S-learner, the Bayesian Causal Forest (BCF), the Causal Forest, and the X-learner exhibited the best performance. In general, the meta-learners with BART and tree-based direct estimation methods performed better than the representation learning methods and the multiple imputation methods. As for the evaluation metrics, [tau subscript risk subscript R] and the Switch Doubly Robust MSE (SDR-MSE) performed the best in identifying the best ITE estimation method when the true treatment effect was unknown. This dissertation contributes to a small but growing body of research on ITE estimation which is gaining popularity in various fields due to its potential for tailoring interventions to the specific needs of individuals and for targeting programs at those who would benefit from them the most. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]