Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Working Knowledge
Business Research for Business Leaders
  • Browse All Articles
  • Popular Articles
  • Cold Call Podcast
  • Managing the Future of Work Podcast
  • About Us
  • Book
  • Leadership
  • Marketing
  • Finance
  • Management
  • Entrepreneurship
  • All Topics...
  • Topics
    • COVID-19
    • Entrepreneurship
    • Finance
    • Gender
    • Globalization
    • Leadership
    • Management
    • Negotiation
    • Social Enterprise
    • Strategy
  • Sections
    • Book
    • Podcasts
    • HBS Case
    • In Practice
    • Lessons from the Classroom
    • Op-Ed
    • Research & Ideas
    • Research Event
    • Sharpening Your Skills
    • What Do You Think?
    • Working Paper Summaries
  • Browse All
    A General Theory of Identification
    06 Apr 2020Working Paper Summaries

    A General Theory of Identification

    by Iavor Bojinov and Guillaume Basse
    Statistical inference teaches us how to learn from data, whereas identification analysis explains what we can learn from it. This paper proposes a simple unifying theory of identification, encouraging practitioners to spend more time thinking about what they can estimate from the data and assumptions before trying to estimate it.
    LinkedIn
    Email

    Author 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.

    Paper Information

    • Full Working Paper Text
    • Working Paper Publication Date: February 2020
    • HBS Working Paper Number: HBS Working Paper #20-086
    • Faculty Unit(s): Technology and Operations Management
      Trending
        • 24 Jan 2023
        • Research & Ideas

        Passion at Work Is a Good Thing—But Only If Bosses Know How to Manage It

        • 17 Jan 2023
        • In Practice

        8 Trends to Watch in 2023

        • 19 Jan 2023
        • Research & Ideas

        What Makes Employees Trust (vs. Second-Guess) AI?

        • 25 Jan 2022
        • Research & Ideas

        More Proof That Money Can Buy Happiness (or a Life with Less Stress)

        • 10 Jan 2023
        • Research & Ideas

        How to Live Happier in 2023: Diversify Your Social Circle

    Iavor I. Bojinov
    Iavor I. Bojinov
    Assistant Professor of Business Administration
    Richard Hodgson Fellow
    Contact
    Send an email
    → More Articles
    Find Related Articles
    • Analysis
    • Research
    • Mathematical Methods

    Sign up for our weekly newsletter

    Interested in improving your business? Learn about fresh research and ideas from Harvard Business School faculty.
    ǁ
    Campus Map
    Harvard Business School Working Knowledge
    Baker Library | Bloomberg Center
    Soldiers Field
    Boston, MA 02163
    Email: Editor-in-Chief
    →Map & Directions
    →More Contact Information
    • Make a Gift
    • Site Map
    • Jobs
    • Harvard University
    • Trademarks
    • Policies
    • Accessibility
    • Digital Accessibility
    Copyright © President & Fellows of Harvard College