To access this work you must either be on the Smith College campus OR have valid Smith login credentials.
On Campus users: To access this work if you are on campus please Select the Download button.
Off Campus users: To access this work from off campus, please select the Off-Campus button and enter your Smith username and password when prompted.
Non-Smith users: You may request this item through Interlibrary Loan at your own library.
Alternative Title
Causality and machine learning
Publication Date
2020
First Advisor
Joseph O'Rourke
Document Type
Honors Project
Degree Name
Bachelor of Arts
Department
Computer Science
Keywords
Explainability, Black box problem, Causality, Machine learning, Pearl's do-calculus, Causal discovery, Causal inference, Practical challenges
Abstract
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.
Rights
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.
Language
English
Recommended Citation
Ha, You Jeen, "Algorithmic (in)explainability : causality and machine learning" (2020). Honors Project, Smith College, Northampton, MA.
https://scholarworks.smith.edu/theses/2221
Smith Only:
Off Campus Download
Comments
vii, 72 pages : illustrations (some color) Includes bibliographical references (pages 59-72)