Template-Type: ReDIF-Paper 1.0 Author-Name: Arne Henningsen Author-Name-First: Arne Author-Name-Last: Henningsen Author-Email: arne@ifro.ku.dk Author-Workplace-Name: Department of Food and Resource Economics, University of Copenhagen Author-Name: Guy Low Author-Name-First: Guy Author-Name-Last: Low Author-Email: guy.low@wur.nl Author-Workplace-Name: Business Economics Group, Wageningen University & Research Author-Name: David Wuepper Author-Name-First: David Author-Name-Last: Wuepper Author-Email: wuepper@uni-bonn.de Author-Workplace-Name: Institute for Food and Resource Economics, University of Bonn Author-Name: Tobias Dalhaus Author-Name-First: Tobias Author-Name-Last: Dalhaus Author-Email: tobias.dalhaus@wur.nl Author-Workplace-Name: Business Economics Group, Wageningen University & Research Author-Name: Hugo Storm Author-Name-First: Hugo Author-Name-Last: Storm Author-Email: hugo.storm@ilr.uni-bonn.de Author-Workplace-Name: Institute for Food and Resource Economics, University of Bonn Author-Name: Dagim Belay Author-Name-First: Dagim Author-Name-Last: Belay Author-Email: dgb@ifro.ku.dk Author-Workplace-Name: Department of Food and Resource Economics, University of Copenhagen Author-Name: Stefan Hirsch Author-Name-First: Stefan Author-Name-Last: Hirsch Author-Email: s.hirsch@uni-hohenheim.de Author-Workplace-Name: Department of Management in Agribusiness, University of Hohenheim Title: Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists 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. Length: 39 pages Creation-Date: 2024-12 File-URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2024/IFRO_WP_2024_03.pdf File-Format: Application/pdf Number: 2024/03 Classification-JEL: C21, C23, C24, C26, C51, C52 Keywords: causal inference, observational data, instrumental variables, difference in differences, regression discontinuity Handle: RePEc:foi:wpaper:2024_03