Machine learning and causality: Building efficient, and reliable models for decision-making
We explore relationships between machine learning (ML) and causal inference. We focus on improvements in each by borrowing ideas from one another. ML has been successfully applied to many problems, but the lack of strong theoretical guarantees has led to many unexpected failures. Models that perf...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139131 |