June 22-23 2017, Centre for Reasoning, University of Kent, Canterbury, UK
Jonathan Bright (University of Oxford)
Jörg Müller (Universitat Oberta de Catalunya)
Chidiebere Ogbonnaya (University of East Anglia)
Wolfgang Pietsch (Technical University of Munich)
Federica Russo (Universiteit van Amsterdam)
Over the past few years, big data has been the focus of considerable research effort. One of the main motivations is the potential for using big data to learn causal relationships. In particular, significant challenges and opportunities arise from big data resulting from human interactions that are recorded via web, sensors and mobile device, or revealed through digitization of historical records. Society faces a transformative data deluge, from which knowledge can be extracted.
However, one can question how big data should be employed in the study of social phenomena, and what the methodological implications are of using it to obtain causal knowledge. What conceptual framework is required to establish causal inferences from big data? Can big data offer evidence of social mechanisms? Which algorithms are appropriate for the analysis of a particular social outcome?
This conference seeks to explore methodological implications of using big data for causal discovery in the social sciences. The conference will bring together philosophers and social scientists.
Call for papers
Researchers with interests in big data and methodology, including PhD candidates and early career researchers, are encouraged to submit an abstract of up to 500 words on or before 1st of March via email to firstname.lastname@example.org. The final decision on submissions will be made by 1st April. Grants will be available to help cover travel costs for contributed speakers.
Contributions should address foundational questions such as the following:
- Which accounts of causality best fit the programme for employing big data for causal discovery?
- How can we get evidence of social mechanisms from big data?
- How can big data enhance our ability to deal with complex phenomena?
- How can data collection techniques affect causal inferences?
- What issues can arise when analysing big data with machine learning algorithms?
- How can data visualizations inform causal models?
Registration is free but compulsory. There are a limited number of places so please register early. Please register via email to email@example.com.
This conference is organised by Virginia Ghiara on behalf of the Centre for Reasoning at the University of Kent and the Eastern ARC Consortium.
For any queries please contact Virginia Ghiara: firstname.lastname@example.org