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What does OLS identify under the zero conditional mean assumption?

Author

Listed:
  • Crudu, Federico

    (University of Siena and CRENoS)

  • Mellace, Giovanni

    (Department of Economics)

  • Smits, Joeri

    (Harvard University)

  • Knaus, Michael

    (School of Business and Economics)

Abstract

Many econometrics textbooks imply that under mean independence of the regressors and the error term, the OLS estimand has a causal interpretation. We provide counterexamples of data-generating processes (DGPs) where the standard assumption of zero conditional mean error is satisfied, but where OLS identifies a pseudo-parameter that does not have a causal interpretation. No such counterexamples can be constructed when the assumption needed is stated in the potential outcome framework, highlighting the fact that causal inference requires causal, and not just stochastic, assumptions.

Suggested Citation

  • Crudu, Federico & Mellace, Giovanni & Smits, Joeri & Knaus, Michael, 2022. "What does OLS identify under the zero conditional mean assumption?," Discussion Papers on Economics 3/2022, University of Southern Denmark, Department of Economics, revised 15 Nov 2022.
    • Handle: RePEc:hhs:sdueko:2022_003
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    Cited by:

    1. Goel, Deepti, 2025. "Estimator of What? A Note on Teaching Regressions in Introductory Econometrics," IZA Discussion Papers 18007, Institute of Labor Economics (IZA).

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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