Theoretical Discussion of Applicability and a Practical Example of Using Statistical Second-Generation Techniques to Analyze Causal Relationships in Animal Experiments

In animal experiments, causal relationships in physiological or disease processes are investigated by using interventions. Applying second-generation statistical methods could be used to identify important links in life processes. This article as a first step describes how second-generation statistical methods that are often used in social sciences are currently applied in veterinary medicine, including a single-animal experimental study, or an ecotoxicity study in fish. It explains how second-generation statistical methods allow flexible modeling to simultaneously calculate causal relationships between constructs in several layers. It continues with a discussion on how theoretical concepts from this statistical approach could be transferred to experimental or medical data. As an applied example, an investigation on a data set analyzed with a second-generation method is presented, showing how this allows us to calculate relationships between variables within a complex theoretical model. Limitations of the use of second-generation statistical methods as strict requirements on the data sets are overcome by technical developments; however, causality cannot be established by statistically testing hypothesized causal structures. Using second-generation statistical methods in the future might promote obtaining more data from one animal and thereby potentially even reducing animals in line with the 3R principle.