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A self-agency bias in preschoolers' causal inferences

Tamar Kushnir, Henry M. Wellman, Susan A. Gelman
2009 Developmental Psychology  
When initial independent effects were probabilistic, and thus subsequent simultaneous actions were causally ambiguous, children showed a self-agency bias.  ...  These results demonstrate that children's own experience of action influences their causal learning, and suggest possible benefits in uncertain and ambiguous everyday learning contexts.  ...  Acknowledgments This research was supported by a fellowship from the McDonnell Collaborative Initiative on Causal Learning and an NICHD post-doctoral fellowship to T.K.  ... 
doi:10.1037/a0014727 pmid:19271843 pmcid:PMC3689272 fatcat:7nya4jwfdreyxatbypcqo64hwq

Comment On Hausman & Woodward On The Causal Markov Condition

Daniel Steel
2006 British Journal for the Philosophy of Science  
Hausman & Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub "modularity" ([1999, 2004]).  ...  In addition, I show that their argument is invalid and trace this invalidity to two features of modularity, namely, that it is stated in terms of pairwise independence and "arrowbreaking" interventions  ...  Acknowledgments I would like to thank Dan Hausman for gracious, in depth correspondence regarding an earlier draft of this essay and two anonymous referees for helpful comments.  ... 
doi:10.1093/bjps/axi154 fatcat:nnnebfqjerhfjhscc6lpfqxiom

Experiences with Modelling Issues in Building Probabilistic Networks [chapter]

Linda C. van der Gaag, Eveline M. Helsper
2002 Lecture Notes in Computer Science  
Since many of these issues pertain not only to our application but are likely to emerge for other applications as well, we feel that sharing them will contribute to engineering probabilistic networks in  ...  As we have developed a large probabilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues.  ...  We are grateful to Babs Taal and Berthe Aleman from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, who provided the domain knowledge for the construction of the oesophagus network.  ... 
doi:10.1007/3-540-45810-7_4 fatcat:ry3yndudsfgc3k5qpbcc6lnd2e

A diagnostic method that uses causal knowledge and linear programming in the application of Bayes' formula

Gregory F. Cooper
1986 Computer Methods and Programs in Biomedicine  
This paper discusses a method for using causal knowledge to structure findings according to their probabilistic dependencies.  ...  One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent.  ...  A fundamentally important aspect of a causal representation is that it can greatly limit the number of possible probabilistic influences on any process or state.  ... 
doi:10.1016/0169-2607(86)90024-6 pmid:3519071 fatcat:c5e7gdkwd5bxbf2gov4jis3754

Belief networks revisited

Judea Pearl
1993 Artificial Intelligence  
versatility and power and are now considered the most common representation scheme for probabilistic knowledge.  ...  the publication of Fusion, although space permits but a sketchy account of the wealth of recent developments in this area. 2  ...  of causal organizations.  ... 
doi:10.1016/0004-3702(93)90169-c fatcat:wbbmxq64rzf5hkorhzjdhn7rgy

Probabilistic causal models of multimorbidity concepts

Martijn Lappenschaar, Arjen Hommersom, Peter J F Lucas
2012 AMIA Annual Symposium Proceedings  
In this paper, we employ causal Bayesian networks to define and analyze a novel framework that can be used to model a spectrum of aspects related to multimorbidity.  ...  ., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex  ...  Concretely, if we have an arc C → E, then we say C causally positively influences E if: P (e | do(c)) > P (e | do(c)) Negative causal influences can be defined similarly.  ... 
pmid:23304319 pmcid:PMC3540573 fatcat:3cnvtqgyujegppkaz7igjm26y4

Causal Graphs and Biological Mechanisms [chapter]

Alexander Gebharter, Marie I. Kaiser
2013 Explanation in the Special Sciences  
In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information.  ...  Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice shows the need for quantitative, probabilistic models of mechanisms  ...  Acknowledgements We would like to thank the members of the research group "Causation and Explanation", the participants of the colloquia at the University of Cologne and at the University of Düsseldorf  ... 
doi:10.1007/978-94-007-7563-3_3 fatcat:odct6mbtdvamxdbjui4e3qbcl4

A Backwards View for Assessment [article]

Ross D. Shachter, David Heckerman
2013 arXiv   pre-print
Much artificial intelligence research focuses on the problem of deducing the validity of unobservable propositions or hypotheses from observable evidence.!  ...  Many of the knowledge representation techniques designed for this problem encode the relationship between evidence and hypothesis in a directed manner.  ...  This type of conditional independence is an important simplifying assumption for the construction and assessment of models of uncertainty. , /"'""' • � , In the influence diagram we always require that  ... 
arXiv:1304.3107v1 fatcat:ob2o4iqd45auhfqzlafoiqfxg4

Causality and Unification: How Causality Unifies Statistical Regularities

Gerhard Schurz
2015 THEORIA : an International Journal for Theory, History and Fundations of Science  
It is demonstrated that not the core of TC but extended versions of TC have empirical content, by means of which they can generate independently testable predictions.  ...  The core axioms of the theory of causal nets (TC) are justified because they give the best if not the only unifying explanation of two statistical phenomena: screening off and linking up.  ...  Acknowledgements This work has been supported by the CRC 991 of the DFG.  ... 
doi:10.1387/theoria.11913 fatcat:qdqrmnbm5zchdnbxkuqscllf4q

A Dynamic Interaction Between Machine Learning and the Philosophy of Science

Jon Williamson
2004 Minds and Machines  
I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy  ...  of science.  ...  The causal Markov condition says that a cause is probabilistically independent of any of its noneffects conditional on its direct causes.  ... 
doi:10.1023/b:mind.0000045990.57744.2b fatcat:7w4jcip6rbbw5bybi6bq3ib3cy

Qualitative Propagation and Scenario-based Explanation of Probabilistic Reasoning [article]

Max Henrion, Marek J. Druzdzel
2013 arXiv   pre-print
Comparing a few of the most probable scenarios provides an approximate way to explain the results of probabilistic reasoning. Both schemes employ causal as well as probabilistic knowledge.  ...  The other, Scenario-based reasoning, involves the generation of alternative causal "stories" accounting for the evidence.  ...  The condition it embodies is sometimes called causal independence, namely that the probability that each present cause is sufficient to produce the effect is independent of the presence or sufficiency  ... 
arXiv:1304.1082v1 fatcat:ftidls3jczenbpheefk5h4hnle

Bayesian network modelling through qualitative patterns

Peter J.F. Lucas
2005 Artificial Intelligence  
In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns.  ...  A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence.  ...  Preliminaries To start, the basic theory of Bayesian networks, causal independence and qualitative probabilistic networks are reviewed.  ... 
doi:10.1016/j.artint.2004.10.011 fatcat:oi6yljawdncc5i6rfedrrfmzou

What Is Wrong With Bayes Nets?

Nancy Cartwright, Sherwood J. B. Sugden
2001 The Monist  
is really causally inefficacious rather than having mixed influence on the effect. 3b.  ...  It tells us that a variable will be probabilistically independent of every other variable except its own effects once all of its direct causes have been conditioned on.  ... 
doi:10.5840/monist20018429 fatcat:axbuifbvrjhczcbnwf3f3daehe

Causal Decision Theory and Decision-theoretic Causation

Christopher Read Hitchcock
1996 Noûs  
of Exact Philosophy in Calgary, and audience members at Princeton University (particularly Dick Jeffrey and David Lewis).  ...  I regret that spatial considerations have prevented me from giving fair treatment to all of their suggestions and challenges.  ...  "The principle invoked here is that causal independence implies probabilistic independence. Perhaps this is making a minimal appeal to a probabilistic theory of causation.  ... 
doi:10.2307/2216116 fatcat:63rwcg7yubadbfspff3cg4kiq4

Causal discovery from medical textual data

S Mani, G F Cooper
2000 Proceedings. AMIA Symposium  
LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z.  ...  Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care.  ...  This work was supported by the National Library of Medicine (training grant LM07059 and grant R01-LM06696) and by the National Science Foundation (grant IIS-9812021).  ... 
pmid:11079942 pmcid:PMC2243738 fatcat:ktevklkkn5ck3kenrecim6mmke
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