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Introduction Introduction
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Machine Learning to Estimate Proxies for Social Science Concepts Machine Learning to Estimate Proxies for Social Science Concepts
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Building Neural Networks from Logistic Regression Building Neural Networks from Logistic Regression
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Effect Estimation or Prediction? Effect Estimation or Prediction?
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Logistic Regression as a Special Case of a Neural Network Logistic Regression as a Special Case of a Neural Network
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A Simulation A Simulation
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Additional Flexibility in Neural Networks Additional Flexibility in Neural Networks
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Demonstrating Neural Networks with Texts and Images Demonstrating Neural Networks with Texts and Images
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Classifying Satellite Images with a Convolutional Neural Network Classifying Satellite Images with a Convolutional Neural Network
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A Recurrent Neural Network on Hate Speech A Recurrent Neural Network on Hate Speech
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Words of Caution Words of Caution
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Acknowledgments Acknowledgments
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References References
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Notes Notes
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An Introduction to Neural Networks for the Social Sciences
Get accessDepartment of Political Science, Washington University in St. Louis
Department of Political Science, Washington University in St. Louis
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Published:18 December 2023
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Abstract
Social scientists now regularly employ computational models, most often to create proxy variables for theoretical concepts that are difficult to observe directly. This chapter discusses how the application of these models differs from traditional quantitative methods common to the social sciences. It then introduces a particular method—neural networks—by building from a model familiar to social scientists: logistic regression. To build intuition about how neural networks flexibly approximate complex functions, the chapter compares the performance of logistic regression to neural networks in a simple simulation. It then further demonstrates the flexibility of neural networks by introducing convolutional and recurrent neural networks and their application to image and text classification, respectively. Throughout, the chapter relates approaches to machine learning with neural networks to common statistical practices in social science. It concludes with a discussion of new methodological challenges posed by reliance on neural networks and open areas of research.
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