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Causality and machine learning
Bachelor of Arts
Explainability, Black box problem, Causality, Machine learning, Pearl's do-calculus, Causal discovery, Causal inference, Practical challenges
Machine learning algorithms, which pervade much of society today, currently lack explainability and transparency, resulting in “The ‘Black Box’ Problem.” I will thereby explore this problem by investigating computer scientist Judea Pearl’s the- ory of causality, a theory grounded in causal models, as well as his “do-calculus” for determining the effects of interventions. The goal here is to understand the power, promise, and possible weaknesses in his theory, which has been accepted by many working on the problem of algorithmic explainability. Although he is not the only researcher of causality, I will concentrate on his theory, which is one of the most influential and the furthest developed. I will also briefly draw on philosophical investigations of causality, as it has been a long-studied topic in numerous traditions of thought. Following this contextual study, we will complete an experimental component with various methods of causal discovery and with PyCausalImpact, a Python library for causal inference. It is important to recognize that algorithmic explainability for ML with causal mechanisms is an active and un- resolved area of research. Nonetheless, based on my examination of Pearl’s theory in the context of current literature on ML and the philosophical analyses of causation, I conclude that current approaches to causal inference with respect to ML are very brittle and do not solve “The ’Black Box’ Problem” with great confidence.
2020 You Jeen Ha. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Ha, You Jeen, "Algorithmic (in)explainability : causality and machine learning" (2020). Honors Project, Smith College, Northampton, MA.
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vii, 72 pages : illustrations (some color) Includes bibliographical references (pages 59-72)