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Causal Relational Learning [article]

Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu
2020 arXiv   pre-print
causality and reasoning about the effect of complex interventions in relational domains.  ...  In this paper, we present a formal framework for causal inference from such relational data.  ...  In this paper, we propose a declarative framework for Causal Relational Learning, a foundation for causal inference over relational domains.  ... 
arXiv:2004.03644v1 fatcat:ymm26dvnkbg4fjjoskm74xn3ke

Learning causality and causality-related learning: some recent progress

Kun Zhang, Bernhard Schölkopf, Peter Spirtes, Clark Glymour
2017 National Science Review  
LEARNING CAUSAL RELATIONS It is well known in statistics that 'causation implies correlation, but correlation does not imply causation'.  ...  CAUSALITY-RELATED MACHINE LEARNING Learning under data heterogeneity has been becoming important because of the potential distribution shift in the data and the expense or neglect of labeling procedures  ... 
doi:10.1093/nsr/nwx137 pmid:30034911 pmcid:PMC6051411 fatcat:aisu2w7n2nev3l3byslhgviygu

Why Represent Causal Relations? [chapter]

Michael Strevens
2007 Causal Learning  
The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability.  ...  A Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations?  ...  makes no reference to causal relations.  ... 
doi:10.1093/acprof:oso/9780195176803.003.0016 fatcat:2un4wuegr5em5fcva7k3dyycr4

Introducción: Aprendizaje de relaciones causales Introduction: Learning of causal relations

Helena Matute
2002 Cognitiva  
También los adultos aprendemos a veces relaciones causales que son ilusorias (como en los casos de comportamiento supersticioso e ilusión de control), otras veces no detectamos relaciones causales que  ...  El aprendizaje de relaciones causales es, probablemente, uno de los más básicos que se pueden dar en todo ser humano.  ...  Necesita de un conocimiento causal previo, así como de un conocimiento de las condiciones de inferencia causal.  ... 
doi:10.1174/021435502753511196 fatcat:grxacxzfizbf3icavisnrr4oce

Learning Transition Models with Time-delayed Causal Relations [article]

Junchi Liang, Abdeslam Boularias
2020 arXiv   pre-print
This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability  ...  of model-based reinforcement learning (RL) techniques.  ...  of the discovered causal relations.  ... 
arXiv:2008.01593v1 fatcat:3toeiadykvdirjmkobaosbilf4

Learning Causal Relations in Multivariate Time Series Data

Pu Chen, Hsiao Chihying
2007 Economics : the Open-Access, Open-Assessment e-Journal  
A TSCM can be seen as a structural VAR identified by the causal relations among the variables.  ...  We also discuss the relation between the probabilistic causal concept presented in TSCMs and the concept of Granger causality.  ...  A two step learning procedure is developed to uncover the potential causal relations implied in the data.  ... 
doi:10.5018/economics-ejournal.ja.2007-11 fatcat:l5zrjhk4qbbgvhhprxmg3jqycy

Learning Causal Relations in Multivariate Time Series Data

Ray C. Fair, Pu Chen
2007 Social Science Research Network  
A TSCM can be seen as a structural VAR identified by the causal relations among the variables.  ...  We also discuss the relation between the probabilistic causal concept presented in TSCMs and the concept of Granger causality.  ...  The third one concerns the efficiency of algorithms to learn causal relations implied in the observed data.  ... 
doi:10.2139/ssrn.1716355 fatcat:hsugyb4unnf65covqfwr32hqy4

Preschoolers learn to switch with causally related feedback

Bianca M.C.W. van Bers, Ingmar Visser, Maartje Raijmakers
2014 Journal of Experimental Child Psychology  
Preschoolers learn to switch with causally related feedback van Bers, B.M.C.W.; Visser, I.; Raijmakers, M.E.J.  ...  Although the results of our study clearly show that children can learn to switch between conflicting sorting rules when given causally related feedback, it is very unlikely that this means we improved  ...  Additional inquiry is needed to examine the far transfer of the effects of causally related feedback on preschoolers' perseverative behavior found in the current study, for example, transfer to related  ... 
doi:10.1016/j.jecp.2014.03.007 pmid:24892884 fatcat:arn3gjprazcgdbbeuscyzxdruu

Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning [article]

Sanghack Lee, Vasant Honavar
2015 arXiv   pre-print
Maier et al. (2010) introduced the relational causal model (RCM) for representing and inferring causal relationships in relational data.  ...  The correctness of the algorithm proposed by Maier et al. (2013a) for learning RCM from data relies on the soundness and completeness of AGG for relational d-separation to reduce the learning of an RCM  ...  and provided a sound and complete causal structure learning algorithm, called the relational causal discovery (RCD) algorithm (Maier et al., 2013a) , for inferring causal relationships from relational  ... 
arXiv:1508.02103v2 fatcat:jntrlz55kvgmnlccvfaiptv224

Toddlers Infer Higher-Order Relational Principles in Causal Learning

Caren M. Walker, Alison Gopnik
2013 Psychological Science  
In conclusion, the current study does suggest that the ability to infer causal higher-order relations, an ability that could play a crucial role in further learning, is in place in humans from a very early  ...  We designed a nonverbal blicket-detector task to explore when children could use higher-order relations to make causal inferences.  ...  In this paradigm, toddlers are able to quickly learn higher-order relational causal principles and use them to guide their actions.  ... 
doi:10.1177/0956797613502983 pmid:24270464 fatcat:ocjydbnhfnc3riqj4ussaeu7b4

Semi-supervised learning of causal relations in biomedical scientific discourse

Claudiu Mihăilă, Sophia Ananiadou
2014 BioMedical Engineering OnLine  
We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers.  ...  Conclusion: Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain.  ...  Nevertheless, to the best of our knowledge, it has not been applied in discourse (causal) relation recognition. The entire learning process is depicted visually in Figure 5 .  ... 
doi:10.1186/1475-925x-13-s2-s1 pmid:25559746 pmcid:PMC4304242 fatcat:kjund6s5ord3dpybpcw5gjyjce

Chimpanzees use observed temporal directionality to learn novel causal relations

Claudio Tennie, Christoph J. Völter, Victoria Vonau, Daniel Hanus, Josep Call, Michael Tomasello
2019 Primates  
We discuss these findings in relation to the literature on causal inferences as well as associative learning.  ...  This outcome suggests that chimpanzees, like human children, do not rely solely on their own actions to make use of novel causal relations, but they can learn causal sequences based on observation alone  ...  If chimpanzees, like 24-month-old human children, can learn directed causal relations from temporal cues alone, we hypothesized, they would prefer the button whose activation preceded the effect (i.e.,  ... 
doi:10.1007/s10329-019-00754-9 pmid:31549268 pmcid:PMC6858906 fatcat:i5enzaebffburic5njk4gq3uzq

Memory-based hypothesis formation: Heuristic learning of commonsense causal relations from text

H Cem Bozsahin
1992 Cognitive Science  
We have designed the learning algorithms to account for networks of causal relations.  ...  The algorithm for learning causal relations from scratch is given in Figure 8 .  ...  This issue is also related to teaching computer programs to learn domain knowledge from text or user dialogues.  ... 
doi:10.1016/0364-0213(92)90028-s fatcat:hqsoccj7dzdkbcl27hnpewf4c4

Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021 [article]

Adrian Ahne, Vivek Khetan, Xavier Tannier, Md Imbessat Hassan Rizvi, Thomas Czernichow, Francisco Orchard, Charline Bour, Andrew Fano, Guy Fagherazzi
2021 arXiv   pre-print
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand  ...  Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related  ...  Machine and deep learning models have also been applied to extract causal relations.  ... 
arXiv:2111.01225v3 fatcat:pexuc4ka55dwnf73yclqqql6hi

Evidence that judgments of learning are causally related to study choice

J. METCALFE, B. FINN
2008 Psychonomic Bulletin & Review  
However, the correlations between JOLs and choices were opposite in these two studies (negative and Evidence that judgments of learning are causally related to study choice Janet Metcalfe and Bridgid  ...  finn Columbia University, New York, New York Three experiments investigated whether study choice was directly related to judgments of learning (JOLs) by examining people's choices in cases in which  ... 
doi:10.3758/pbr.15.1.174 pmid:18605499 fatcat:utn7b2ocdfb3vbchhnywbsj5t4
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