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
email@example.com. The final decision on submissions will be made by 1st
April. Grants will be available to help cover travel costs for contributed
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
- 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 firstname.lastname@example.org.
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: email@example.com