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Estimating highdimensional directed acyclic graphs with the pcalgorithm
 Journal of Machine Learning Research
, 2005
"... We consider the PCalgorithm (Spirtes et al., 2000) for estimating the skeleton and equivalence class of a very highdimensional directed acyclic graph (DAG) with corresponding Gaussian distribution. The PCalgorithm is computationally feasible and often very fast for sparse problems with many nodes ..."
Abstract

Cited by 50 (5 self)
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We consider the PCalgorithm (Spirtes et al., 2000) for estimating the skeleton and equivalence class of a very highdimensional directed acyclic graph (DAG) with corresponding Gaussian distribution. The PCalgorithm is computationally feasible and often very fast for sparse problems with many nodes (variables), and it has the attractive property to automatically achieve high computational efficiency as a function of sparseness of the true underlying DAG. We prove uniform consistency of the algorithm for very highdimensional, sparse DAGs where the number of nodes is allowed to quickly grow with sample size n, as fast as O(n a) for any 0 < a < ∞. The sparseness assumption is rather minimal requiring only that the neighborhoods in the DAG are of lower order than sample size n. We also demonstrate the PCalgorithm for simulated data. Keywords: asymptotic consistency, DAG, graphical model, PCalgorithm, skeleton 1.
Estimating highdimensional intervention effects from observation data. The Ann
 of Stat
"... We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can ..."
Abstract

Cited by 8 (2 self)
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We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in highdimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study and on a data set about riboflavin production. 1. Introduction. Our
Ockham’s Razor, Empirical Complexity, and Truthfinding Efficiency
 THEORETICAL COMPUTER SCIENCE
, 2007
"... The nature of empirical simplicity and its relationship to scientific truth are longstanding puzzles. In this paper, empirical simplicity is explicated in terms of empirical effects, which are defined in terms of the structure of the inference problem addressed. Problem instances are classified acc ..."
Abstract

Cited by 6 (6 self)
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The nature of empirical simplicity and its relationship to scientific truth are longstanding puzzles. In this paper, empirical simplicity is explicated in terms of empirical effects, which are defined in terms of the structure of the inference problem addressed. Problem instances are classified according to the number of empirical effects they present. Simple answers are satisfied by simple worlds. An efficient solution achieves optimum worstcase cost over each complexity class with respect to such costs such as the number of retractions or errors prior to convergence and elapsed time to convergence. It is shown that always choosing the simplest theory compatible with experience and hanging onto it while it remains simplest is both necessary and sufficient for efficiency.
arXiv: 0810.4214 ESTIMATING HIGHDIMENSIONAL INTERVENTION EFFECTS FROM OBSERVATIONAL DATA
, 810
"... We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can ..."
Abstract
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We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in highdimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study, and on a data set about riboflavin production. 1. Introduction. Our
Efficiency
, 2007
"... The nature of empirical simplicity and its relationship to scientific truth are longstanding puzzles. In this paper, empirical simplicity is explicated in terms of empirical effects, which are defined in terms of the structure of the inference problem addressed. Problem instances are classified acc ..."
Abstract
 Add to MetaCart
The nature of empirical simplicity and its relationship to scientific truth are longstanding puzzles. In this paper, empirical simplicity is explicated in terms of empirical effects, which are defined in terms of the structure of the inference problem addressed. Problem instances are classified according to the number of empirical effects they present. Simple answers are satisfied by simple worlds. An efficient solution achieves optimum worstcase cost over each complexity class with respect to such costs such as the number of retractions or errors prior to convergence and elapsed time to convergence. It is shown that always choosing the simplest theory compatible with experience and hanging onto it while it remains simplest is both necessary and sufficient for efficiency. 1 The Simplicity Puzzle Machine learning, statistics, and the philosophy of science all recommend the selection of simple theories or models on the basis of empirical data, where simplicity has something to do with minimizing independent entities, principles, causes, or equational