
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
The PC algorithm learns maximally oriented causal Bayesian networks. How...
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Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets
In this paper, we discuss structure learning of causal networks from mul...
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A MetaTransfer Objective for Learning to Disentangle Causal Mechanisms
We propose to metalearn causal structures based on how fast a learner a...
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Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
In recent years, there has been an increasing interest in studying causa...
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Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift. ...
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The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Causal inference is central to many areas of artificial intelligence, in...
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Ancestral causal learning in high dimensions with a human genomewide application
We consider learning ancestral causal relationships in high dimensions. ...
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Amortized learning of neural causal representations
Causal models can compactly and efficiently encode the datagenerating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called causal relational networks (CRN) for learning causal models using neural networks. The CRN represent causal models using continuous representations and hence could scale much better with the number of variables. These models also take in previously learned information to facilitate learning of new causal models. Finally, we propose a decodingbased metric to evaluate causal models with continuous representations. We test our method on synthetic data achieving high accuracy and quick adaptation to previously unseen causal models.
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Comments
Robert R Tucci ∙
Your definition of Bayesian networks is too limited. Bayesian Networks can have continuous nodes and also deterministic nodes. Such nodes have been used by B net practitioners since the beginning of B nets. In the continuous node case, one assigns a transition matrix to the node which is a probability density instead of a discrete probability distribution. Andrew Gelman (Columbia Univ.) has been using continuous nodes in his B nets his entire career. As for deterministic nodes, if the node outputs y and the input is x, then the transition probability matrix for the node is \delta(x, f(y)), where \delta is either the Kronecker or the Dirac delta function, and f(\cdot) is a function of x. A delta function is a perfectly legal probability distribution.
So the distinctions you are making are fallacious. Neural nets are Bayesian networks too! They are very narrow class of B nets in which all of the nodes are deterministic.
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