Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs

Autor:	Luttermann, Malte, Braun, Tanya, Möller, Ralf, Gehrke, Marcel
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Data Structures and Algorithms Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the Proceedings of the 16th International Conference on Scalable Uncertainty Management (SUM 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.07006 Zobrazit plný text záznamu View this record from Arxiv