Data regularly recorded in commercial herds have been used extensively for estimation of disease incidence rates, for inferences regarding genetic and phenotypic associations between traits, or for developing predictive models for economically important traits. Some studies have also used field data to investigate potential causal relationships between variables. However, inferring causal effects from observational data is complex due to potential confounding effects and careful analyses using specific statistical and data mining techniques as well as different sets of assumptions are required. Nonetheless, although virtually unknown in the agricultural research community, such methods are available and have been used in many other fields. In this paper, we review and discuss the analysis of observational data using field-recorded information and its potential utility in the study of causal effects in livestock. It is our postulation that there is much to be learned from such data, which can be used either to explicitly investigate causal relationships between variables or to generate hypotheses for further investigation using controlled experiments or additional field-recorded data.