2026-09-19T00:50:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1641522025-12-04T03:06:44Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Barzilay, Regina
Jaakkola, Tommi S.
Wu, Menghua
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-12-03T16:11:14Z
2025-12-03T16:11:14Z
2025-05
2025-08-14T19:45:42.671Z
https://hdl.handle.net/1721.1/164152
Scientific research revolves around the discovery and validation of causal relationships between variables. Machine learning has the potential to increase the efficiency of this process by proposing novel hypotheses from data observations, or by designing experiments that maximize success rate. This thesis addresses these problems through pragmatic approaches, designed to model large systems and incorporate rich domain knowledge. These algorithms are applied to use cases in molecular biology and drug discovery, which highlight their potential to inform efficient experiment design and to automate the analysis of experimental results.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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https://rightsstatements.org/page/InC-EDU/1.0/
Practical Algorithms for Modeling Causality to Accelerate Scientific Discovery
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>Scientific research revolves around the discovery and validation of causal relationships between variables. Machine learning has the potential to increase the efficiency of this process by proposing novel hypotheses from data observations, or by designing experiments that maximize success rate. This thesis addresses these problems through pragmatic approaches, designed to model large systems and incorporate rich domain knowledge. These algorithms are applied to use cases in molecular biology and drug discovery, which highlight their potential to inform efficient experiment design and to automate the analysis of experimental results.</Abstract>
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