Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916879620112384 |
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| author | Henningsen, Arne Low, Guy Wuepper, David Dalhaus, Tobias Storm, Hugo Belay, Dagim Hirsch, Stefan |
| author_facet | Henningsen, Arne Low, Guy Wuepper, David Dalhaus, Tobias Storm, Hugo Belay, Dagim Hirsch, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists Henningsen, Arne Low, Guy Wuepper, David Dalhaus, Tobias Storm, Hugo Belay, Dagim Hirsch, Stefan Econometrics Methodology 62D20 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. |
| title | Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists |
| topic | Econometrics Methodology 62D20 |
| url | https://arxiv.org/abs/2508.02310 |