Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
This work addresses the need for more flexible causal models in machine learning and statistics by reducing prior knowledge requirements, though it appears incremental as it builds directly on existing parametric factor graph frameworks.
The paper tackles the problem of performing causal inference in partially directed graphs by introducing partially directed parametric causal factor graphs (PPCFGs), which extend previous fully directed models, and demonstrates how lifting can be applied to speed up inference while maintaining exact answers.
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.