*Please disseminate as appropriate.*
Causal Reasoning Workshop 2019
------------------------------
Co-located with 18TH Mexican International Conference on Artificial
Intelligence (MICAI 2019)
28 October 2019
Xalapa, Mexico
Website: https://ccc.inaoep.mx/~bio/2019_MICAI_CausalityWorkshop/
Contact: f.orihuela-esp...@inaoep.mx
== Call for Papers ==
The formal study of causality started about 300 hundred years ago with
the works of the great philosophers David Hume and Immanuel Kant. Hume
approached causality from an empirical perspective: the knowledge that a
certain thing (the potential cause) causes or prevents another (the
effect) is acquired by means of experience and without any prior
knowledge. He identified 3 conditions for this to happen: temporal and
spatial contiguity, precedence in time of the cause with respect to the
effect and constant conjunction; i.e., the constant occurrence of the
both of them. The notion of covariation summarizes such a perspective.
Kant, on the other hand, focused on the notion of causal power, which
refers to the knowledge that some mechanism or power can cause a certain
effect. As can be inferred, this is what we refer to as prior knowledge.
Of course, both approaches have their respective advantages and
disadvantages. Although the former have given us important clues for
capturing the essential features of causality, the latter represent a
serious challenge to overcome. Since then, different disciplines such as
Philosophy, Psychology, Statistics and Artificial Intelligence (AI) have
studied this important phenomenon. One of AI’s main interest is to build
intelligent systems capable of automatically acquiring cause-effect
relationships and using causal knowledge to build better intelligent
systems.
This workshop aims at bringing together researchers and students from
Artificial Intelligence, Statistics and Cognitive Science who work on
causal theory, the construction of causal models and their evaluation,
as well in learning causal models from data. We welcome original
research as well as work in progress in the following (but not limited)
topics:
- Theoretical models of causation
- Structural Equation Models
- Causal Bayesian networks and other graphical causal models
- Metrics and benchmarks for assessing causality
- Causal discovery
- Causal inference
- Machine learning and causality
- Applications of causal models
You can also see our CFP in:
- EasyChair https://easychair.org/CReW2019
- WikiCFP http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=92123
== About the workshop ==
The workshop will be held as a satellite event in MICAI 2019
(http://www.micai.org/2019/)
Duration: 1 day. Expected 8h. Program to be announced.
== Submissions ==
Contributions should be formatted according to Springer LNCS format. We
accept 2 types of contributions:
- Short (6 pgs) - Intended for early research and preliminary results
- Long (12 pgs) - Intended for solid pieces of research.
Submissions will be made through EasyChair. Please submit your
contribution through https://easychair.org/conferences/?conf=crew2019
== Important Dates ==
Paper submission: 15 September 2019
Notification of decision: 30 September 2019
Camera ready: 15 October 2019
Event: 28 October 2019
== Venue ==
The conference will be held at the Centro de Investigación en
Inteligencia Artificial en Xalapa, Veracruz, Mexico.
== Organizers ==
Luis Enrique Sucar, INAOE
Nicandro Cruz-Ramírez, CIIA-UV,
Felipe Orihuela-Espina, INAOE
Samuel Montero, INAOE
Jenny Betsabé Vázquez-Aguirre, CIIA-UV
== Program Committee Members ==
(Alphabetical order by surname)
Nicandro Cruz-Ramírez, CIIA-UV, Mexico
Daniel Malinsky, Johns Hopkins University, USA
Samuel Montero, INAOE, Mexico
Eduardo Morales, INAOE, Mexico
Felipe Orihuela-Espina, INAOE, Mexico
Rubén Sánchez-Romero, Rutgers University-Newark, USA
Luis Enrique Sucar, INAOE, Mexico
Jenny Betsabé Vázquez-Aguirre, CIIA-UV, Mexico
We look forward to receiving your manuscripts and welcoming you to our
workshop.
Best regards,
The organizing committee
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