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    JMLR
    2010
    134views more  JMLR 2010»
    Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
    13 years 1 days ago
    Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
    Download  parallel.vub.ac.be
    This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
    Jan Lemeire, Kris Steenhaut
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    JMLR
    2010
    93views more  JMLR 2010»
    Sufficient covariates and linear propensity analysis
    13 years 1 days ago
    Sufficient covariates and linear propensity analysis
    Download  jmlr.csail.mit.edu
    Working within the decision-theoretic framework for causal inference, we study the properties of "sufficient covariates", which support causal inference from observation...
    Hui Guo, A. Philip Dawid
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    JMLR
    2010
    143views more  JMLR 2010»
    Beware of the DAG!
    13 years 1 days ago
    Beware of the DAG!
    Download  clopinet.com
    Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
    A. Philip Dawid
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    UAI
    1994
    117views Artificial Intelligence» more  UAI 1994»
    A New Look at Causal Independence
    13 years 6 months ago
    A New Look at Causal Independence
    Download  research.microsoft.com
    Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction whe...
    David Heckerman, John S. Breese
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    CAS
    2008
    104views more  CAS 2008»
    Identifiability in Causal Bayesian Networks: a Gentle Introduction
    13 years 5 months ago
    Identifiability in Causal Bayesian Networks: a Gentle Introduction
    Download  www.cse.sc.edu
    Marco Valtorta, Yimin Huang
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