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

Arne Henningsen, Guy Low, David Wuepper, Tobias Dalhaus, Hugo Storm, Dagim Belay and Stefan Hirsch
Additional contact information
Arne Henningsen: Department of Food and Resource Economics, University of Copenhagen, Denmark
Guy Low: Business Economics Group, Wageningen University & Research, The Netherlands
Tobias Dalhaus: Business Economics Group, Wageningen University & Research, The Netherlands
Hugo Storm: Institute for Food and Resource Economics, University of Bonn, Germany
Dagim Belay: Department of Food and Resource Economics, University of Copenhagen, Denmark

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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., income, crop yields, pollution). Only some of these research questions can be studied experimentally. Most empirical studies in agricultural and applied economics thus rely on observational data. However, estimating causal effects with observational data requires appropriate research designs and a transparent discussion of all identifying assumptions, together with empirical evidence to assess the probability that they hold. 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.

Date: 2025-08
New Economics Papers: this item is included in nep-agr and nep-ecm
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Working Paper: Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2508.02310

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