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This paper concerns the assessment of the effects of actions or policies from a combination of: (i) nonexperimental data, and (ii) causal assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called " causal graph " , in which some variables are presumed to be unobserved. The paper establishes new criteria for deciding(More)
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the gen­ erated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independen­ cies, as read through the d-separation crite­ rion, and functional constraints, for which no(More)
We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We derive expressions for the Bayesian score that a causal structure should obtain from streams of data produced by locally changing distributions. Simulation experiments indicate that dynamic information(More)
BACKGROUND Several studies in the literature have investigated the possible role of the extent of lymphadenectomy in gastric cancer treatment failure. The current study attempted to determine the effectiveness and safety of lymphadenectomy with gastrectomy for the treatment of gastric cancer. METHODS Randomized controlled trials (RCTs) were identified by(More)
Hypohidrotic ectodermal dysplasia (HED) is characterized by severe hypohidrosis, hypotrichosis, and hypodontia. It can be inherited in autosomal dominant, autosomal recessive, or X-linked patterns. Mutations in the EDA gene, which encodes ectodysplasin-A, are responsible for X-linked HED (XLHED). In the present study, we identified a Chinese Han family with(More)
We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the un­ derlying data-generating model. We ana­ lyze the classes of structures that are equiv­ alent relative to a stream of distributions produced by local changes, and devise algo­ rithms that output graphical representations of these(More)
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real(More)
We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnet-work, e.g., a single edge, in O(n2 n) time and the posterior probabilities for all n(n − 1) potential edges in O(n2 n) total time, assuming that the(More)