Many public-policy studies (Martin and Scott 2020) use randomized field experiments for drawing causal conclusions (e.g., Chen et al. 2020). A typical randomized field experiment involves a control group and a treatment group to which individual units (e.g., consumers, patients) are randomly assigned, after which an intervention is implemented in the treatment group. An intervention could be a marketing program to which only units in the treatment group are exposed. To assess the intervention's efficacy, researchers typically estimate the average treatment effect computed as the mean difference in the outcome between the units in the treatment group and the control group. When applying the results of a randomized experiment, it is assumed that the treatment effect within the manipulated condition is the same for all the units assigned to the treatment condition. This may not always be the case, as the effect may differ for subgroups within a treatment (subgroup differences).
Editorial Introduction: field experiments and public policy