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oai:dspace.mit.edu:1721.1/1630102026-01-21T18:53:55Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Chlipala, Adam Zhang, Anna Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2025-10-06T17:39:26Z 2025-10-06T17:39:26Z 2025-05 2025-06-23T14:04:43.185Z https://hdl.handle.net/1721.1/163010 Many foundational tools in causal inference are based on graphical structure and can involve complex conditions that obscure the underlying causal logic. Given the inherent complexity and subtlety of cause-and-effect phenomena, establishing formal guarantees about these tools is both challenging and important. This thesis presents a semantics-driven formalization of causal models within the Coq proof assistant, enabling precise, mechanized reasoning about causal relationships. Central to this work is a new function-based definition of conditional independence, which captures how changes propagate through a causal graph. We prove that this semantic notion is equivalent to the standard graphical criterion of d-separation, thereby establishing a rigorous bridge between structural and semantic interpretations of independence. The formalization includes a library of graph-theoretic and causal-reasoning tools, encompassing key concepts such as mediators, confounders, and colliders. By linking the syntactic and semantic perspectives on causality, this work lays a robust foundation for formally verifying causal assumptions and guiding experimental design. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Formalizing Causal Models Through the Semantics of Conditional Independence Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="dc015d6b-ec67-4689-b74b-bb14073741e3"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Formalizing Causal Models Through the Semantics of Conditional Independence</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Zhang, Anna</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Many foundational tools in causal inference are based on graphical structure and can involve complex conditions that obscure the underlying causal logic. Given the inherent complexity and subtlety of cause-and-effect phenomena, establishing formal guarantees about these tools is both challenging and important. This thesis presents a semantics-driven formalization of causal models within the Coq proof assistant, enabling precise, mechanized reasoning about causal relationships. Central to this work is a new function-based definition of conditional independence, which captures how changes propagate through a causal graph. We prove that this semantic notion is equivalent to the standard graphical criterion of d-separation, thereby establishing a rigorous bridge between structural and semantic interpretations of independence. The formalization includes a library of graph-theoretic and causal-reasoning tools, encompassing key concepts such as mediators, confounders, and colliders. By linking the syntactic and semantic perspectives on causality, this work lays a robust foundation for formally verifying causal assumptions and guiding experimental design.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>