Canali, Stefano and Ratti, Emanuele (2023) Between quantity and quality: competing views on the role of Big Data for causal inference. [Preprint]
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Abstract
When does more data help and when does it not in the sciences? In the past decade, this question has become central because of the phenomenon of Big Data. While these discussions started as a result of somewhat naive ideas that have been closely analyzed and mostly rejected in the philosophy of data, the question about the epistemic difference that more or less data make still matters, especially in light of the impressive performance of data science tools, which seem to improve their performance the more data are trained on. In several areas of the sciences, having more data is connected to methodological and epistemic benefits and something that research should strive towards. More data is often equated to better science: this elicits crucial questions about the epistemic value of the quantity of data. In this chapter, we discuss this problem in light of current discussions in the life and health sciences and the philosophy of data.
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| Item Type: | Preprint | |||||||||
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| Keywords: | Big Data; causal inference; genomics; epidemiology; induction | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Causation General Issues > Confirmation/Induction Specific Sciences > Medicine |
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| Depositing User: | Dr Emanuele Ratti | |||||||||
| Date Deposited: | 12 Nov 2023 00:09 | |||||||||
| Last Modified: | 12 Nov 2023 00:09 | |||||||||
| Item ID: | 22763 | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Causation General Issues > Confirmation/Induction Specific Sciences > Medicine |
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| Date: | 2023 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/22763 |
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