Understanding of the brain and the principles governing neural processing requirestheories that are parsimonious, can account for a diverse set of phenomena, and can maketestable predictions. Here, we review the theory of Bayesian causal inference, which hasbeen tested, refined, and extended in a variety of tasks in humans and other primates byseveral research groups. Bayesian causal inference is a normative model and hasaccounted for human behavior in a vast number of tasks including unisensory andmultisensory perceptual tasks, sensorimotor, and motor tasks, and has accounted forcounter-intuitive findings. The theory has made novel predictions that have been testedand confirmed empirically, and recent studies have started to map the algorithms andneural implementation of the model in the human brain. The parsimony, the diversity ofthe phenomena that the model has explained, and its illuminating brain function at allthree of Marr's levels of analysis make Bayesian causal inference a strong neurosciencetheory. The progress made in understanding Bayesian causal inference in the brain hasbeen due to the contributions and collaboration of many labs around the worldspecializing in different research topics and methods exploring different aspects of brainfunction. Overall, this highlights the importance of collaborative and multi-disciplinaryresearch for the development of new theories in neuroscience.