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⏲ Duration: 44 min 38 sec
✓ Published: 20-Dec-2020
✓ Published: 20-Dec-2020
Description:
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). Researchers in our fields are yet to fully recognize the value of ML techniques for theory building from data. This may be in part due to scholars’ inherent distaste for
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). Researchers in our fields are yet to fully recognize the value of ML techniques for theory building from data. This may be in part due to scholars’ inherent distaste for
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