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- Jin Tian, Judea Pearl
- AAAI/IAAI
- 2002

This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive 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 a necessary and… (More)

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)

- Jin Tian, Judea Pearl
- UAI
- 2002

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)

- Jin Tian
- 2001

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)

- Jin Tian, Judea Pearl
- UAI
- 2001

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)

- Jin Tian
- UAI
- 2000

This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL) principle, for a given (consistent) variable ordering. The algorithm exhaustively searches through all network structures and guarantees to find the network… (More)

- Jin Tian, Ru He, Lavanya Ram
- UAI
- 2010

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)

- Jin Tian
- UAI
- 2004

This paper concerns the assessment of the effects of actions from a combination of nonexperimen-tal data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other… (More)

- Yuan Jun Teng, Shu Mei Pan, +4 authors Jian Xu Han
- Surgical endoscopy
- 2011

BACKGROUND
Mesh fixation during laparoscopic total extraperitoneal (TEP) inguinal hernia repair is still controversial. Although many surgeons considered it necessary to fix the mesh, some published studies supported elimination of mesh fixation. Therefore, a meta-analysis based on randomized controlled trials (RCTs) was conducted to compare the… (More)