643 research outputs found
Learning AMP Chain Graphs under Faithfulness
This paper deals with chain graphs under the alternative
Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a
constraint based algorithm for learning an AMP chain graph a given probability
distribution is faithful to. We also show that the extension of Meek's
conjecture to AMP chain graphs does not hold, which compromises the development
of efficient and correct score+search learning algorithms under assumptions
weaker than faithfulness
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
An intervention may have an effect on units other than those to which it was
administered. This phenomenon is called interference and it usually goes
unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg
and Andersson-Madigan-Perlman chain graphs to create a new class of causal
models that can represent both interference and non-interference relationships
for Gaussian distributions. Specifically, we define the new class of models,
introduce global and local and pairwise Markov properties for them, and prove
their equivalence. We also propose an algorithm for maximum likelihood
parameter estimation for the new models, and report experimental results.
Finally, we show how to compute the effects of interventions in the new models.Comment: v2: Section 6 has been added. v3: Sections 7 and 8 have been added.
v4: Major reorganization. v5: Major reorganization. v6-v7: Minor changes. v8:
Addition of Appendix B. v9: Section 7 has been rewritte
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