Much of our foundational knowledge about caribou is based on observational and quasi-experimental studies. This can lead to biased predictions of the benefits of management actions, due to statistical over- or under-control, selection bias and misapplication of synthetic variables. Causal analysis is a method that is used in other disciplines to improve inferences when robust experimental designs are infeasible or unethical. It can identify causal relationships in observational data based on a set of identification rules, if strong assumptions are met. The approach has theoretical advantages over standard regression techniques and makes assumptions more explicit. I apply the method to several case studies to demonstrate how causal analysis can be used to reduce biases and improve policy recommendations for caribou recovery.