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Estimating Causal Effects With Observational Data: Guidelines for Agricultural and Applied Economists

Author

Listed:
  • Arne Henningsen
  • Guy Low
  • David Wuepper
  • Tobias Dalhaus
  • Hugo Storm
  • Dagim Belay
  • Stefan Hirsch

Abstract

Most research questions in agricultural and applied economics are causal in nature: they study how changes in one or more variables (such as policies, prices or weather) affect one or more other variables (e.g., income, crop yields or pollution). Only a minority of these research questions can be studied with experimental methods, so most empirical studies in agricultural and applied economics rely on observational data. However, estimating causal effects with observational data requires an appropriate research design and a transparent discussion of all identifying assumptions, together with a critical discussion of how plausible they are. This paper provides an overview of approaches that are frequently used in agricultural and applied economics to estimate causal effects with observational data. It then provides advice and guidelines for agricultural and applied economists seeking to estimate causal effects with observational data, including how to assess and discuss the identification strategies adopted in their analysis.

Suggested Citation

  • Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2026. "Estimating Causal Effects With Observational Data: Guidelines for Agricultural and Applied Economists," Journal of Agricultural Economics, Wiley Blackwell, vol. 77(2), pages 356-382, June.
    • Handle: RePEc:bla:jageco:v:77:y:2026:i:2:p:356-382
    DOI: 10.1111/1477-9552.70019
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    Cited by:

    1. is not listed on IDEAS
    2. David Wuepper & Wyclife Agumba Oluoch & Hadi Hadi, 2025. "Satellite Data in Agricultural and Environmental Economics: Theory and Practice," Agricultural Economics, International Association of Agricultural Economists, vol. 56(3), pages 493-511, May.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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