Robust, reliable and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of standard black box prediction algorithms. New mathematical connections between distributional robustness and causality provide methodological paths for improving the reliability and understanding of machine learning algorithms, with wide-ranging prospects for various applications.