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  • 2020
  • Working Paper
  • HBS Working Paper Series

A General Theory of Identification

By: Iavor Bojinov and Guillaume Basse
  • Format:Print
  • | Language:English
  • | Pages:27
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Abstract

What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective. This definition raises important questions: Are parameters the only quantities that can be identified? Is the concept of identification meaningful outside of parametric statistics? Does it even require the notion of a statistical model? Partial and idiosyncratic answers to these questions have been discussed in econometrics, biological modeling, and in some subfields of statistics like causal inference. This paper proposes a unifying theory of identification that incorporates existing definitions for parametric and nonparametric models and formalizes the process of identification analysis. The applicability of this framework is illustrated through a series of examples and two extended case studies.

Keywords

Identification; Econometric Models; Analytics and Data Science; Theory

Citation

Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
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About The Author

Iavor I. Bojinov

Technology and Operations Management
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  • Causal Inference By: Iavor Bojinov, Michael Parzen and Paul J. Hamilton
  • Prediction & Machine Learning By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
  • Linear Regression By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
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