The Frugal Inference of Causal Relations

Forster, Malcolm R.; Raskutti, Garvesh; Stern, Reuben; Weinberger, Naftali

doi:10.1093/bjps/axw033

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…CBL relies on the assumption of faithfulness, which has been challenged by numerous authors [Cartwright, 2001, Steel, 2006, Zhang and Spirtes, 2008, Andersen, 2013, Uhler et al, 2013. Several weaker variants have been proposed, including SGS-minimality [Spirtes et al, 2000], P-minimality [Pearl, 2009a], frugality [Forster et al, 2018], and 2-adjacency faithfulness [Marx et al, 2021]. One direction for future work is to extend CBL under these relaxed assumptions.…”

Section: Discussionmentioning

confidence: 99%

Causal discovery under a confounder blanket

Watson¹,

Silva²

2022

Preprint

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Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discovery algorithms typically require parametric restrictions or extreme sparsity constraints. We relax these assumptions and focus on an important but more specialized problem, namely recovering a directed acyclic subgraph of variables known to be causally descended from some (possibly large) set of confounding covariates, i.e. a confounder blanket. This is useful in many settings, for example when studying a dynamic biomolecular subsystem with genetic data providing causally relevant background information. Under a structural assumption that, we argue, must be satisfied in practice if informative answers are to be found, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity. We derive a sound and complete algorithm for identifying causal relationships under these conditions and implement testing procedures with provable error control for linear and nonlinear systems. We demonstrate our approach on a range of simulation settings.

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Section: Discussionmentioning

confidence: 99%

Causal discovery under a confounder blanket

Watson¹,

Silva²

2022

Preprint

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“…This last point suggests important implications functional robustness may have for a particular aspect of causal discovery in the context of graphical modeling-namely, the Causal Faithfulness Condition (Spirtes et al 2000, Zhang and Spirtes 2008, Forster et al 2018, Weinberger 2018). 20 Causal discovery involves inference between the causal structure of a system and the joint probability distribution over all variables in that system.…”

Section: Answering Mitchell's Challenge: Robustness and Cyclicitymentioning

confidence: 99%

Robustness and Modularity

Boone¹

2024

The British Journal for the Philosophy of Science

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Functional robustness refers to a system's ability to maintain a function in the face of perturbations to the causal structures that support performance of that function. Modularity, a crucial element of standard methods of causal inference and difference-making accounts of causation, refers to the independent manipulability of causal relationships within a system. Functional robustness appears to be at odds with modularity. If a function is maintained despite manipulation of some causal structure that supports that function, then the relationship between that structure and function fails to be manipulable independent of other causal relationships within the system. Contrary to this line of reasoning, I argue that functional robustness often attends feedback control, rather than failures of modularity. Feedback control poses its own challenges to causal explanation and inference, but those challenges do not undermine modularity-and indeed, modularity is crucial to grappling with them.

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“…Forster et al [7] referred to the above assumption as the (unique) frugality assumption, and argued that it has multiple desirable properties compared to faithfulness. Under the SMR assumption, the SP method [32] has been shown to produce asymptotically correct results.…”

Section: Causal Bayesian Network and Common Assumptionsmentioning

confidence: 99%

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

Zheng

Zhang

et al. 2022

Preprint

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Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the superstructure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.Recently, NOTEARS [52] casts the Bayesian network structure learning task into a continuous constrained optimization problem with the least squares objective, using an algebraic characterization of directed acyclic graph (DAG). Subsequent work GOLEM [23] adopts a continuous unconstrained optimization formulation with a likelihood-based objective. For NOTEARS, it remains unclear 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

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The Frugal Inference of Causal Relations

Cited by 31 publications

References 16 publications

Causal discovery under a confounder blanket

Causal discovery under a confounder blanket

Robustness and Modularity

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

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