Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915013883592704 |
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| author | Luttermann, Malte Braun, Tanya Möller, Ralf Gehrke, Marcel |
| author_facet | Luttermann, Malte Braun, Tanya Möller, Ralf Gehrke, Marcel |
| contents | Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs Luttermann, Malte Braun, Tanya Möller, Ralf Gehrke, Marcel Artificial Intelligence Data Structures and Algorithms Machine Learning Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships. |
| title | Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs |
| topic | Artificial Intelligence Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2411.07006 |