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Journal of Machine Learning Research, ISSN 1532-4435, 01/2015, Volume 15, pp. 3741 - 3782
Journal Article
Journal of the Royal Statistical Society: Series B (Statistical Methodology), ISSN 1369-7412, 07/2019, Volume 81, Issue 3, pp. 459 - 487
Summary We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings... 
High dimensional consistency | Confounding | Causal structure learning | Structured sparsity | Causality | Directed acyclic graphs | Equivalence | Markov processes | Faith
Journal Article
Journal of Statistical Software, ISSN 1548-7660, 04/2012, Volume 47, Issue 11, pp. 1 - 26
The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this... 
Graphical model | Do-calculus | Causality | RFCI | FCI | IDA | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | graphical model | do-calculus | STATISTICS & PROBABILITY | NETWORKS | DISCOVERY | causality
Journal Article
The Annals of Statistics, ISSN 0090-5364, 12/2009, Volume 37, Issue 6A, pp. 3133 - 3164
Journal Article
The Annals of Statistics, ISSN 0090-5364, 6/2015, Volume 43, Issue 3, pp. 1060 - 1088
We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs... 
Causal inference | DIRECTED ACYCLIC GRAPHS | LATENT | CPDAG | STATISTICS & PROBABILITY | DAG | MARKOV EQUIVALENCE | hidden confounders | MAG | PAG | covariate adjustment | SELECTION | EQUIVALENCE CLASSES | 62H99
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 05/2018, Volume 18, pp. 1 - 62
We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic... 
Confounding | Latent variables | Graphical models | Covariate adjustment | Causal effects | confounding | DIRECTED ACYCLIC GRAPHS | latent variables | CAUSAL INFERENCE | graphical models | LATENT | covariate adjustment | causal effects | CONFOUNDER | SELECTION | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Journal Article
Journal of Statistical Planning and Inference, ISSN 0378-3758, 12/2013, Volume 143, Issue 12, pp. 2068 - 2071
Journal Article
Journal of Computational and Graphical Statistics, ISSN 1061-8600, 06/2005, Volume 14, Issue 2, pp. 352 - 362
Journal Article
Annual Review of Statistics and Its Application, ISSN 2326-8298, 3/2017, Volume 4, Issue 1, pp. 365 - 393
Journal Article
BMC Medical Research Methodology, ISSN 1471-2288, 2010, Volume 10, Issue 1, pp. 14 - 14
Background: Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is... 
DISABILITY | HEALTH | INTERNATIONAL CLASSIFICATION | INSTRUMENTS | ALGORITHM | HEALTH CARE SCIENCES & SERVICES | Disability Evaluation | Models, Theoretical | Health Status Indicators | Humans | Disabled Persons - classification | Quality of Life | Health Status | Medicine, Experimental | Medical research | Usage | Graphic methods | Methods
Journal Article