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Estimating causal effects is crucial in domains like healthcare, economics, and education. Despite advances in machine learning (ML) for estimating conditional average treatment effects (CATE), the practical adoption of these methods remains limited, due to the complexities of implementing, tuning, and validating them. To address these challenges, we formalize the search for an optimal ML pipeline for CATE estimation as a counterfactual Combined Algorithm Selection and Hyperparameter (CASH) optimization. We introduce AutoCATE, the first end-to-end, automated solution for CATE estimation. Unlike prior approaches that address only parts of this problem, AutoCATE integrates evaluation, estimation, and ensembling in a unified framework. AutoCATE enables comprehensive comparisons of different protocols, yielding novel insights into CATE estimation and a final configuration that outperforms commonly used strategies. To facilitate broad adoption and further research, we release AutoCATE as an open-source software package.