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

Author

Listed:
  • Arne Henningsen

    (Department of Food and Resource Economics, University of Copenhagen)

  • Guy Low

    (Business Economics Group, Wageningen University & Research)

  • David Wuepper

    (Institute for Food and Resource Economics, University of Bonn)

  • Tobias Dalhaus

    (Business Economics Group, Wageningen University & Research)

  • Hugo Storm

    (Institute for Food and Resource Economics, University of Bonn)

  • Dagim Belay

    (Department of Food and Resource Economics, University of Copenhagen)

  • Stefan Hirsch

    (Department of Management in Agribusiness, University of Hohenheim)

Abstract

Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., the welfare of individuals or the society, the demanded or produced quantity, pollution). Only a small number of these research questions can be studied with economic experiments such as randomised controlled trials (RCTs), lab experiments or lab-in-the-field experiments. Hence, most empirical studies in agricultural and applied economics use observational data. However, estimating causal effects with observational data requires appropriate research designs and convincing identification strategies, which are usually very difficult or even impossible to devise. Likely as a consequence, in the applied economics literature, it can commonly be observed that results are interpreted as causal despite lacking a robust identification strategy, which has contributed to a credibility crisis in economics research. This paper provides an overview of various 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 who are intending to estimate causal effects with observational data, e.g., how to assess and discuss the chosen identification strategies in their publications.

Suggested Citation

  • Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
  • Handle: RePEc:foi:wpaper:2024_03
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    Keywords

    causal inference; observational data; instrumental variables; difference in differences; regression discontinuity;
    All these keywords.

    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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