• Corpus ID: 197538291

Causation, Prediction, and Search, Second Edition

  title={Causation, Prediction, and Search, Second Edition},
  author={Peter L. Spirtes and Clark Glymour and Richard Scheines},
  booktitle={Adaptive computation and machine learning},

The PC Algorithm and the Inference to Constitution

The aim of this paper is to show that Gebharter's proposal incurs severe problems, ultimately rooted in the widespread non-compliance of mechanistic systems with PC’s assumptions, which casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC's assumptions.

AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms

The decomposition-based approach for learning Bayesian networks (BNs) proposed by (Xie et al., 2006) is extended to learn AMP CGs, which include BNs as a special case, under the faithfulness assumption, and the results of both algorithms are more accurate and stabler when the sample size is reasonably large and the underlying graph is sparse.

Properties, Learning Algorithms, and Applications of Chain Graphs and Bayesian Hypergraphs

This thesis proposes new feasible and efficient structure learning algorithms to learn chain graphs from data under the faithfulness assumption and considers a more general graphical structure, namely directed acyclic graph.

A Causal Approach to the Study of TCP Performance

This article presents a methodology to overcome the challenges of working with real-world data and extend the application of causality to complex systems in the area of telecommunication networks, for which assumptions of normality, linearity and discrete data do no hold.

Causal Discovery from Temporal Data: An Overview and New Perspectives

  • Chang GongDi YaoChuzhe ZhangWenbin LiJingping Bi
  • Computer Science
  • 2023
The correlation between the two categories of temporal data casual discovery is specified and a systematical overview of existing solutions is provided and public datasets, evaluation metrics and new perspectives are provided.

Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance

This work collects CausalMT, a dataset where the MT training data are also labeled with the human translation directions, and shows that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.

Causal Structural Learning Via Local Graphs

A new local notion of sparsity is proposed for consistent structure learning in the presence of latent and selection variables, and a new version of the Fast Causal Inference algorithm is developed with reduced computational and sample complexity, which is referred to as local FCI (lFCI).

Mining Markov Blankets Without Causal Sufficiency

This paper adopts a maximal ancestral graph (MAG) model to represent latent common causes and the concept of MBs without assuming causal sufficiency, and proposes an effective and efficient algorithm to discover the MB of a target variable in an MAG.

Dynamic Relationships and Price Discovery of Western Alfalfa Markets

Alfalfa hay exports have substantially increased since 2007 with 99% being shipped from western ports (Putnam et al., 2013), and likely more than 95% of it originating from seven western states

Automated Search for Causal Relations : Theory and Practice