Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?

Open access
Date
2021Type
- Journal Article
Citations
Cited 10 times in
Web of Science
Cited 11 times in
Scopus
ETH Bibliography
yes
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Abstract
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000498484Publication status
publishedExternal links
Journal / series
Organization ScienceVolume
Pages / Article No.
Publisher
Institute for Operations Research and the Management SciencesSubject
machine learning; algorithmic induction; theory buildingOrganisational unit
03719 - von Krogh, Georg / von Krogh, Georg
03719 - von Krogh, Georg / von Krogh, Georg
Funding
169441 - Leading and Coordinating Knowledge Creation in Online Communities (SNF)
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Show all metadata
Citations
Cited 10 times in
Web of Science
Cited 11 times in
Scopus
ETH Bibliography
yes
Altmetrics