Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
Causal Reasoning with Ancestral Graphs
The Journal of Machine Learning Research
Complete Identification Methods for the Causal Hierarchy
The Journal of Machine Learning Research
Markov Properties for Linear Causal Models with Correlated Errors
The Journal of Machine Learning Research
Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation
The Journal of Machine Learning Research
Identifiability in causal Bayesian networks: a sound and complete algorithm
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Identification of joint interventional distributions in recursive semi-Markovian causal models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Finding a causal ordering via independent component analysis
Computational Statistics & Data Analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks: a unification for discrete and Gaussian domains
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A discovery algorithm for directed cyclic graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
Bayesian probabilities for constraint-based causal discovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
From Association Analysis to Causal Discovery
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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The goal of many sciences is to understand the mechanisms by which variables came to take on the values they have (that is, to find a generative model), and to predict what the values of those variables would be if the naturally occurring mechanisms were subject to outside manipulations. The past 30 years has seen a number of conceptual developments that are partial solutions to the problem of causal inference from observational sample data or a mixture of observational sample and experimental data, particularly in the area of graphical causal modeling. However, in many domains, problems such as the large numbers of variables, small samples sizes, and possible presence of unmeasured causes, remain serious impediments to practical applications of these developments. The articles in the Special Topic on Causality address these and other problems in applying graphical causal modeling algorithms. This introduction to the Special Topic on Causality provides a brief introduction to graphical causal modeling, places the articles in a broader context, and describes the differences between causal inference and ordinary machine learning classification and prediction problems.