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

Most research questions in agricultural and applied economics are causal in nature: they study how changes in one or more variables (such as policies, prices or weather) affect one or more other variables (e.g., income, crop yields or pollution). Only a minority of these research questions can be studied with experimental methods, so most empirical studies in agricultural and applied economics rely on observational data. However, estimating causal effects with observational data requires an appropriate research design and a transparent discussion of all identifying assumptions, together with a critical discussion of how plausible they are. This paper provides an overview of 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 seeking to estimate causal effects with observational data, including how to assess and discuss the identification strategies adopted in their analysis.