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299
Active Learning for Structure in Bayesian Networks
 IN INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2001
"... The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper ..."
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Cited by 75 (2 self)
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The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper
Active Learning of Causal Bayes Net Structure
, 2001
"... We propose a decision theoretic approach for deciding which interventions to perform so as to learn the causal structure of a model as quickly as possible. Without such interventions, it is impossible to distinguish between Markov equivalent models, even given infinite data. We perform online MCMC t ..."
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Cited by 61 (1 self)
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We propose a decision theoretic approach for deciding which interventions to perform so as to learn the causal structure of a model as quickly as possible. Without such interventions, it is impossible to distinguish between Markov equivalent models, even given infinite data. We perform online MCMC to estimate the posterior over graph structures, and use importance sampling to find the best action to perform at each step. We assume the data is discretevalued and fully observed.
Theorybased causal induction
 In
, 2003
"... Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various s ..."
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Cited by 56 (19 self)
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Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the cooccurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computationallevel analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domaingeneral statistical inference guided by domainspecific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.
Bayesian models of cognition
"... For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational a ..."
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Cited by 54 (2 self)
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For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty
Discovering Hidden Variables: A StructureBased Approach
"... A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the ..."
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Cited by 50 (5 self)
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A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this paper, we address the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models. A very natural approach is to search for “structural signatures” of hidden variables — substructures in the learned network that tend to suggest the presence of a hidden variable. We make this basic idea concrete, and show how to integrate it with structuresearch algorithms. We evaluate this method on several synthetic and reallife datasets, and show that it performs surprisingly well.
Exact bayesian structure learning from uncertain interventions
 AI & Statistics, In
, 2007
"... We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities p(Gij = 1D) of a DAG G given data D, to the case where the data is obtained by interventions (experiments). In particular, we consider the case w ..."
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Cited by 46 (5 self)
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We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities p(Gij = 1D) of a DAG G given data D, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP + 05, EW06, WGH06]. 1
Learning graphical model structure using L1regularization paths
 IN PROCEEDINGS OF THE 21ST CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI
, 2007
"... Sparsitypromoting L1regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability ..."
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Cited by 44 (2 self)
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Sparsitypromoting L1regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a pernode basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1based approaches are compared to current state of the art methods on 10 datasets.
Improved learning of Bayesian networks
 Proc. of the Conf. on Uncertainty in Artificial Intelligence
, 2001
"... Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. Therefore, the search space of Bayesian network structures may be organized in equivalence classes, where each of them represents a different set o ..."
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Cited by 40 (5 self)
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Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. Therefore, the search space of Bayesian network structures may be organized in equivalence classes, where each of them represents a different set of conditional independencies. The collection of sets of conditional independencies obeys a partial order, the socalled “inclusion order.” This paper discusses in depth the role that the inclusion order plays in learning the structure of Bayesian networks. In particular, this role involves the way a learning algorithm traverses the search space. We introduce a condition for traversal operators, the inclusion boundary condition, which, when it is satisfied, guarantees that the search strategy can avoid local maxima. This is proved under the assumptions that the data is sampled from a probability distribution which is faithful to an acyclic digraph, and the length of the sample is unbounded. The previous discussion leads to the design of a new traversal operator and two new learning algorithms in the context of heuristic search and the Markov Chain Monte Carlo method. We carry out a set of experiments with synthetic and realworld data that show empirically the benefit of striving for the inclusion order when learning Bayesian networks from data.
Learning causal Bayesian network structures from experimental data
, 2006
"... We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Orderspace MCMC, equienergy sampling, importance weighting and streambased computation are combined to create a fast algorithm for learning causal Bayesian ne ..."
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Cited by 37 (1 self)
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We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Orderspace MCMC, equienergy sampling, importance weighting and streambased computation are combined to create a fast algorithm for learning causal Bayesian network structures.