Experimental Design Issues in Big Data. The Question of Bias
Methodology
2018-11-21 v4
Abstract
Data can be collected in scientific studies via a controlled experiment or passive observation. Big data is often collected in a passive way, e.g. from social media. In studies of causation great efforts are made to guard against bias and hidden confounders or feedback which can destroy the identification of causation by corrupting or omitting counterfactuals (controls). Various solutions of these problems are discussed, including randomization.
Cite
@article{arxiv.1712.06916,
title = {Experimental Design Issues in Big Data. The Question of Bias},
author = {Elena Pesce and Eva Riccomagno and Henry P. Wynn},
journal= {arXiv preprint arXiv:1712.06916},
year = {2018}
}