CReW2019: Causal Reasoning Workshop 2019 Centro de Investigación en Inteligencia Artificial Xalapa, Mexico, October 28, 2019 |
Conference website | https://ccc.inaoep.mx/~bio/2019_MICAI_CausalityWorkshop/ |
Submission link | https://easychair.org/conferences/?conf=crew2019 |
Submission deadline | September 23, 2019 |
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.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. Contributions should be formatted according to Springer LNCS format. We accept 2 types of contributions:
- Full papers - Long (12 pgs) - Intended for solid pieces of research.
- Posters - Short (6 pgs) - Intended for early research and preliminary results
List of Topics
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
Organizing Committee
- Luis Enrique Sucar, INAOE
- Nicandro Cruz-Ramírez, CIIA-UV
- Felipe Orihuela-Espina, INAOE
- Eduardo Morales, INAOE
- Samuel Montero, INAOE
- Rubén Sánchez-Romero, Rutgers University-Newark
- Daniel Malinsky, Johns Hopkins University
- Jenny Betsabé Vázquez-Aguirre, CIIA-UV
Venue
The conference will be held at the Centro de Investigación en Inteligencia Artificial en Xalapa, Veracruz, Mexico.
Contact
All questions about submissions should be emailed to f.orihuela-espina at inaoep.mx