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Econometrics of Text and Large Language Models with Python: Measurement and Causal Inference from Unstructured Data - Brossura
Sinossi
Reactive Publishing
Econometrics of Text and Large Language Models with Python presents practical methods for applying econometric techniques to unstructured text data using modern language models.
The book focuses on three core tasks: measuring latent concepts from text, generating predictions from textual sources, and conducting causal inference with unstructured data. It shows how to move from raw text to reliable quantitative results while maintaining the standards of econometric rigor.
Readers will find clear explanations of the underlying statistical principles alongside working Python implementations. Topics include text representation, embedding-based measurement, predictive modeling with language models, and strategies for identifying causal effects when the primary data source is text. Emphasis is placed on validation, robustness checks, and the practical limits of these methods.
This book is written for applied researchers, data scientists, and quantitative analysts who work with textual data and need reproducible, transparent approaches grounded in econometrics. It assumes familiarity with basic statistics and Python but does not require prior expertise in natural language processing or large language models.
By combining established econometric frameworks with current language model tools, the book provides a structured path for extracting meaningful, verifiable insights from unstructured text.
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Econometrics of Text and Large Language Models with Python: Measurement and Causal Inference from Unstructured Data
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