Arne Henningsen, Guy Low, David Wuepper, Tobias Dalhaus, Hugo Storm, Dagim Belay, Stefan Hirsch
arXiv 4 Aug 2025 · Econometrics · publishedJournal of Agricultural Economics (2025) · 10 citations (OpenAlex)
arXiv:2508.02310 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Aïhounton, G. and Henningsen, A (2024) Does organic farming jeopardize food security of farm households in Benin? self | 0.928 | 5 | 4 | 80% |
| 2 | Lal, A., Lockhart, M., Xu, Y., and Zu, Z (2024) How much should we trust instrumental variable estimates in political science? Practical advice based on 67 replicated studies | 0.874 | 18 | 2 | 100% |
| 3 | Gibson, J (2019) Are you estimating the right thing? An editor reflects | 0.874 | 6 | 2 | 100% |
| 4 | Cattaneo, M. D. and Titiunik, R (2022) Regression discontinuity designs | 0.874 | 6 | 2 | 100% |
| 5 | Angrist, J. and Pischke, J.-S (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.794 | 6 | 3 | 50% |
| 6 | Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., and Wa… (2021) Synthetic difference-in-differences | 0.737 | 3 | 2 | 100% |
| 7 | Callaway, B. and Sant’Anna, P (2021) Difference-in-differences with multiple time periods | 0.693 | 9 | 1 | 100% |
| 8 | Borusyak, K., Jaravel, X., and Spiess, J (2024) Revisiting event study designs: Robust and efficient estimation | 0.693 | 5 | 1 | 100% |
| 9 | Keane, M. and Neal, T (2024) A practical guide to weak instruments | 0.693 | 5 | 1 | 100% |
| 10 | Sun, L. and Abraham, S (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.693 | 5 | 1 | 100% |
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