Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models

Yi Han, Joseph Ramsey, Peter Spirtes
Proceedings of the Fourth Conference on Causal Learning and Reasoning, PMLR 275:1316-1346, 2025.

Abstract

Methods of statistically testing the accuracy of causal graphical models have traditionally been limited, with most focusing on parametric global assessments of the entire causal graph. However, whether or not a causal graphical model passes a statistical test, it is crucial for many practical applications to find which parts of the graph are accurately reconstructed and which are not. In this paper, we introduce the Vertex Checker, the only statistical test that we are aware of that takes as input a causal graphical model G, a vertex X, and an alpha level, sample data, and a conditional independence test, and provides a non-parametric, asymptotically correct, statistical test of a local subgraph of X, is computationally feasible for dozens of variables, and is extendable to other kinds of causal graphical models. Through extensive simulations, we demonstrate the robustness of the Vertex Checker across various data types, causal graphs, and distributions both in terms of accuracy of graphical structure and of quantitative estimates of causal effects. Furthermore, we apply the Vertex Checker to the real-world Sachs dataset, showcasing its practical applicability in uncovering accurate substructures within causal graphs, even when the overall causal graphical model is rejected.

Cite this Paper

BibTeX
@InProceedings{pmlr-v275-han25a, title = {Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models}, author = {Han, Yi and Ramsey, Joseph and Spirtes, Peter}, booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning}, pages = {1316--1346}, year = {2025}, editor = {Huang, Biwei and Drton, Mathias}, volume = {275}, series = {Proceedings of Machine Learning Research}, month = {07--09 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v275/main/assets/han25a/han25a.pdf}, url = {https://proceedings.mlr.press/v275/han25a.html}, abstract = {Methods of statistically testing the accuracy of causal graphical models have traditionally been limited, with most focusing on parametric global assessments of the entire causal graph. However, whether or not a causal graphical model passes a statistical test, it is crucial for many practical applications to find which parts of the graph are accurately reconstructed and which are not. In this paper, we introduce the Vertex Checker, the only statistical test that we are aware of that takes as input a causal graphical model G, a vertex X, and an alpha level, sample data, and a conditional independence test, and provides a non-parametric, asymptotically correct, statistical test of a local subgraph of X, is computationally feasible for dozens of variables, and is extendable to other kinds of causal graphical models. Through extensive simulations, we demonstrate the robustness of the Vertex Checker across various data types, causal graphs, and distributions both in terms of accuracy of graphical structure and of quantitative estimates of causal effects. Furthermore, we apply the Vertex Checker to the real-world Sachs dataset, showcasing its practical applicability in uncovering accurate substructures within causal graphs, even when the overall causal graphical model is rejected.} }
Endnote
%0 Conference Paper %T Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models %A Yi Han %A Joseph Ramsey %A Peter Spirtes %B Proceedings of the Fourth Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Biwei Huang %E Mathias Drton %F pmlr-v275-han25a %I PMLR %P 1316--1346 %U https://proceedings.mlr.press/v275/han25a.html %V 275 %X Methods of statistically testing the accuracy of causal graphical models have traditionally been limited, with most focusing on parametric global assessments of the entire causal graph. However, whether or not a causal graphical model passes a statistical test, it is crucial for many practical applications to find which parts of the graph are accurately reconstructed and which are not. In this paper, we introduce the Vertex Checker, the only statistical test that we are aware of that takes as input a causal graphical model G, a vertex X, and an alpha level, sample data, and a conditional independence test, and provides a non-parametric, asymptotically correct, statistical test of a local subgraph of X, is computationally feasible for dozens of variables, and is extendable to other kinds of causal graphical models. Through extensive simulations, we demonstrate the robustness of the Vertex Checker across various data types, causal graphs, and distributions both in terms of accuracy of graphical structure and of quantitative estimates of causal effects. Furthermore, we apply the Vertex Checker to the real-world Sachs dataset, showcasing its practical applicability in uncovering accurate substructures within causal graphs, even when the overall causal graphical model is rejected.
APA
Han, Y., Ramsey, J. & Spirtes, P.. (2025). Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models. Proceedings of the Fourth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 275:1316-1346 Available from https://proceedings.mlr.press/v275/han25a.html.

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