| Summary: | <p>This work focuses on the intersection of machine learning and causal inference and the way in which the two fields can enhance each other by sharing ideas: utilizing machine learning techniques for the computation of causal quantities, the use of ideas from causal inference for invariant predictions under unseen treatment regimes, and the exploration of topics in trustworthy machine learning, including interpretability and fairness, with a causal lens. In each one of the presented works, we grappled with the strength of assumptions needed to utilize causal inference techniques and relax portions of them when possible.</p>
<p>In Chapter 1, we introduce the motivation behind the works and the challenges that sparked this plan of study. Chapter 2 provides a foundation on basic topics in causal machine learning and trustworthy machine learning. In Chapter 3, we introduce a causal effect estimation method under partial causal graph knowledge. In Chapter 4, we look at causal effect estimation in complex data settings, such as images, text, and gene expression networks, and propose an invariant estimation approach utilizing crude interventions. In Chapter 5, we provide a causal perspective on explainable machine learning, unifying existing works and providing a sound and complete algorithm involving the concepts of sufficiency and necessity. Finally, in Chapters 6 and 7, we introduce methods and investigations in fair machine learning.</p>
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