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Probabilistic Reasoning across the Causal Hierarchy [article]

Duligur Ibeling, Thomas Icard
2021 arXiv   pre-print
We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages.  ...  do-calculus reasoning for causal effects, and the third capturing a fully expressive do-calculus for arbitrary counterfactual queries.  ...  DGE-1656518, and by the Center for the Study of Language and Information.  ... 
arXiv:2001.02889v5 fatcat:y35w6gyjybhsnevhujmlzieju4

Probabilistic Reasoning Across the Causal Hierarchy

Duligur Ibeling, Thomas Icard
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages.  ...  do-calculus reasoning for causal effects, and the third capturing a fully expressive do-calculus for arbitrary counterfactual queries.  ...  Moving to the second and third levels of the hierarchy, Thm. 5 also presents the first combined axiomatization for probabilistic reasoning about causal counterfactuals.  ... 
doi:10.1609/aaai.v34i06.6577 fatcat:mmu64kwb4zfkxahiq7site3b2a

How to Model Mechanistic Hierarchies

Lorenzo Casini
2016 Philosophy of Science  
In this paper, I defend the RBN account from the criticism and argue that it offers a better representation of mechanistic hierarchies than the rival account.  ...  The recursive Bayesian network (RBN) formalism was put forward as a means to model mechanistic hierarchies (Casini et al., 2011) by decomposing variables.  ...  Acknowledgments I wish to thank the participants to the Biological Interest Group of the Lake Geneva, where a prior version of this paper was discussed on 28 October 2014.  ... 
doi:10.1086/687877 fatcat:sw2gw67irbhcxbspaitxkmw6ru

Safety vs. efficacy assessment of pharmaceuticals: Epistemological rationales and methods

Barbara Osimani
2014 Preventive medicine reports  
: the recent debate on the causal association between paracetamol and asthma.  ...  I will address here the latter dimension and present recent proposals to amend evidence hierarchies for the purpose of safety assessment of pharmaceuticals; I then relate these suggestions to a case study  ...  Acknowledgments Funding for the paper has been provided by an intramural project conducted at the University of Camerino (# IM 250300): "Epistemic Asymmetries in Benefit vs.  ... 
doi:10.1016/j.pmedr.2014.08.002 pmid:26844033 pmcid:PMC4721437 fatcat:sjpwbzcgrvhj3jwttcghtihd34

Intuitive Theories as Grammars for Causal Inference [chapter]

Joshua B. Tenenbaum, Thomas L. Griffiths, Sourabh Niyogi
2007 Causal Learning  
causal structure at multiple levels of theoretical abstraction, and what processes of inference connect those knowledge levels to support learning and reasoning across the hierarchy?  ...  A dataset d consists of the observed portions of M instances of some system, d = {x Figure 4 . 4 A hierarchical probabilistic model corresponding to the hierarchy of abstraction in causal theories shown  ... 
doi:10.1093/acprof:oso/9780195176803.003.0020 fatcat:p2ht7xcejfdzfjrey5kqd5nz7y

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models [article]

Matej Zečević, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting
2021 arXiv   pre-print
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference.  ...  Providing an arbitrarily intervened causal graph as input, effectively subsuming Pearl's do-operator, the gate function predicts the parameters of the SPN.  ...  It benefited from the Hessian research priority programme LOEWE within the project WhiteBox, the HMWK cluster project "The Third Wave of AI." and the Collaboration Lab "AI in Construction" (AICO).  ... 
arXiv:2102.10440v5 fatcat:7instaliz5amblf42rdnxdnkay

Causal Assessment of Pharmaceutical Treatments: Why Standards of Evidence Should not be the Same for Benefits and Harms?

Barbara Osimani, Fiorenzo Mignini
2014 Drug Safety  
and harms should not be the same A methodological framework for probabilistic (vs. categorical) causal assessment is needed  ...  To illustrate this, Cartwright's distinction into clinching and vouching methods is adopted and a series of reasons is provided for preferring the latter to the former: (1) the need to take into account  ...  it probabilistically.  ... 
doi:10.1007/s40264-014-0249-5 pmid:25519721 fatcat:ctbj6fyxqbgghh6h2ya7jtgcie

Toward the neural implementation of structure learning

D Gowanlock R Tervo, Joshua B Tenenbaum, Samuel J Gershman
2016 Current Opinion in Neurobiology  
Animals categorize objects, learn to vocalize and may even estimate causal relationships -all in the face of data that is often ambiguous and sparse.  ...  Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience.  ...  Alla Karpova for significant contributions and Shaul Druckmann, Maksim Manakov, Mikhail Proskurin, Vivek Jayaraman and other colleagues at Janelia Research Campus for comments on and assistance with the  ... 
doi:10.1016/j.conb.2016.01.014 pmid:26874471 fatcat:z5jf2k4tc5aozkajjwgqe2twfm

How to Grow a Mind: Statistics, Structure, and Abstraction

J. B. Tenenbaum, C. Kemp, T. L. Griffiths, N. D. Goodman
2011 Science  
Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought  ...  In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it?  ...  Generative models must be probabilistic to handle the learner's uncertainty about the true states of latent variables and the true causal processes at work.  ... 
doi:10.1126/science.1192788 pmid:21393536 fatcat:sh4diud5l5g6hkdw7u2eptlpou

Bayesian networks, Bayesian learning and cognitive development

Alison Gopnik, Joshua B. Tenenbaum
2007 Developmental Science  
Acknowledgements The writing of this article was supported by the James S. McDonnell Foundation Causal Learning Research Collaborative, and the Paul E. Newton Career Development Chair (JBT).  ...  hierarchies.  ...  These probabilistic models can be used to reason and make predictions about the variables when the graph structure is known, and also to learn the graph structure when it is unknown, by observing which  ... 
doi:10.1111/j.1467-7687.2007.00584.x pmid:17444969 fatcat:fang2mkkyndermokwyt7tekvzi

Cognitive Identity Management: Synthetic Data, Risk and Trust

S. Yanushkevich, A. Stoica, P. Shmerko, W. Howells, K. Crockett, R. Guest
2020 2020 International Joint Conference on Neural Networks (IJCNN)  
This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction.  ...  For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model  ...  The authors acknowledge Eur Ing Phil Phillips, CEng, for useful suggestions.  ... 
doi:10.1109/ijcnn48605.2020.9207385 dblp:conf/ijcnn/YanushkevichSSH20 fatcat:zogkameyzvbxpkppq446pmq6gy

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

Predictive coding and thought

Daniel Williams
2018 Synthese  
we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality-the specific way in which concepts productively combine to  ...  I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality-the fact that  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s11229-018-1768-x fatcat:7jxz6rbul5alvi5nmkxdq5v5xi

Emergence and Evidence: A Close Look at Bunge's Philosophy of Medicine

Rainer J. Klement, Prasanta S. Bandyopadhyay
2019 Philosophies  
necessary nor sufficient to establish the truth of a causal claim; (iii) testing of causal hypotheses requires taking into account background knowledge and the context within which an intervention is  ...  Bunge supports the views of the evidence-based medicine movement that randomized controlled trials (RCTs) provide the best evidence to establish the truth of causal hypothesis; in fact, he argues that  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/philosophies4030050 fatcat:nwg7w2e6i5dzzaucpawgqunyfe

Epistemic Gains and Epistemic Games: Reliability and Higher Order Evidence in Medicine and Pharmacology [chapter]

Barbara Osimani
2020 Boston Studies in the Philosophy of Science  
of causation in medicine and in the soft sciences in general, and favours probabilistic approaches to scientific inference, as better equipped for defeasibility of causal inference in such domains.  ...  The former is focused on reliability as minimisation of random and systematic error, and is grounded on a categorical approach to causal assessment, whereas the latter is more focused on the high context-sensitivity  ...  Finally, I thank Adam LaCaze, Scott Podolsky, David Teira for reading and commenting on earlier drafts of the paper, with the usual disclaimer that any errors are my own responsibility.  ... 
doi:10.1007/978-3-030-29179-2_15 fatcat:aefaacee6zhsxbybb4ezbuqmmm
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