Structure Learning for Cyclic Linear Causal Models
- URL: http://arxiv.org/abs/2006.05978v2
- Date: Wed, 19 Aug 2020 20:52:25 GMT
- Title: Structure Learning for Cyclic Linear Causal Models
- Authors: Carlos Am\'endola, Philipp Dettling, Mathias Drton, Federica Onori,
Jun Wu
- Abstract summary: We consider the problem of structure learning for linear causal models based on observational data.
We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders.
- Score: 5.567377163246147
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the problem of structure learning for linear causal models based
on observational data. We treat models given by possibly cyclic mixed graphs,
which allow for feedback loops and effects of latent confounders. Generalizing
related work on bow-free acyclic graphs, we assume that the underlying graph is
simple. This entails that any two observed variables can be related through at
most one direct causal effect and that (confounding-induced) correlation
between error terms in structural equations occurs only in absence of direct
causal effects. We show that, despite new subtleties in the cyclic case, the
considered simple cyclic models are of expected dimension and that a previously
considered criterion for distributional equivalence of bow-free acyclic graphs
has an analogue in the cyclic case. Our result on model dimension justifies in
particular score-based methods for structure learning of linear Gaussian mixed
graph models, which we implement via greedy search.
Related papers
- Parameter identification in linear non-Gaussian causal models under general confounding [8.273471398838533]
We study identification of the linear coefficients when such models contain latent variables.
Our main result is a graphical criterion that is necessary and sufficient for deciding generic identifiability of direct causal effects.
We report on estimations based on the identification result, explore a generalization to models with feedback loops, and provide new results on the identifiability of the causal graph.
arXiv Detail & Related papers (2024-05-31T14:39:14Z) - Causal Modeling with Stationary Diffusions [89.94899196106223]
We learn differential equations whose stationary densities model a system's behavior under interventions.
We show that they generalize to unseen interventions on their variables, often better than classical approaches.
Our inference method is based on a new theoretical result that expresses a stationarity condition on the diffusion's generator in a reproducing kernel Hilbert space.
arXiv Detail & Related papers (2023-10-26T14:01:17Z) - NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning [8.20217860574125]
We propose a novel framework for learning nonlinear cyclic causal models from interventional data, called NODAGS-Flow.
We show significant performance improvements with our approach compared to state-of-the-art methods with respect to structure recovery and predictive performance.
arXiv Detail & Related papers (2023-01-04T23:28:18Z) - Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes [51.84122462615402]
We introduce a novel method to learn the structure and mechanisms of the causal model using Variational Bayes-DAG-GFlowNet.
We extend the method of Bayesian causal structure learning using GFlowNets to learn the parameters of a linear-Gaussian model.
arXiv Detail & Related papers (2022-11-04T21:57:39Z) - Learning Relational Causal Models with Cycles through Relational
Acyclification [16.10327013845982]
We introduce textitrelational acyclification, an operation specifically designed for relational models.
We show that under the assumptions of relational acyclification and $sigma$-faithfulness, the relational causal discovery algorithm RCD is sound and complete for cyclic models.
arXiv Detail & Related papers (2022-08-25T17:00:42Z) - Amortized Inference for Causal Structure Learning [72.84105256353801]
Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test.
We train a variational inference model to predict the causal structure from observational/interventional data.
Our models exhibit robust generalization capabilities under substantial distribution shift.
arXiv Detail & Related papers (2022-05-25T17:37:08Z) - A Unified Experiment Design Approach for Cyclic and Acyclic Causal
Models [32.88438123861557]
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
We propose an experiment design approach that can learn both cyclic and acyclic graphs.
We show that our approach is optimal in terms of the size of the largest experiment required for uniquely identifying the causal graph in the worst case.
arXiv Detail & Related papers (2022-05-20T10:58:41Z) - Sequential Learning of the Topological Ordering for the Linear
Non-Gaussian Acyclic Model with Parametric Noise [6.866717993664787]
We develop a novel sequential approach to estimate the causal ordering of a DAG.
We provide extensive numerical evidence to demonstrate that our procedure is scalable to cases with possibly thousands of nodes.
arXiv Detail & Related papers (2022-02-03T18:15:48Z) - Estimation of Bivariate Structural Causal Models by Variational Gaussian
Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
Causal mechanisms can be described by structural causal models.
One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
arXiv Detail & Related papers (2021-09-06T14:52:58Z) - Structural Landmarking and Interaction Modelling: on Resolution Dilemmas
in Graph Classification [50.83222170524406]
We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas''
We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
arXiv Detail & Related papers (2020-06-29T01:01:42Z) - A Critical View of the Structural Causal Model [89.43277111586258]
We show that one can identify the cause and the effect without considering their interaction at all.
We propose a new adversarial training method that mimics the disentangled structure of the causal model.
Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
arXiv Detail & Related papers (2020-02-23T22:52:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.