Abstract

The problem of deviant causal chains is a classic challenge in the philosophy of action. According to the causal theory of action (CTA), an event qualifies as an action if it is caused by the agent’s intention. In cases of deviant causal chains, this condition is met, but the agent loses control of the situation. To address this, theorists suggest that the intention must cause the action “in the right way”. However, defining what constitutes the “right way” is difficult, as the distinction between having and not having control can be subtle. In this paper, I demonstrate that brain-computer interfaces (BCIs) provide important insights into basic causal deviance. I examine how existing strategies might account for deviant causation in BCI use and highlight their challenges. I advocate for reliability strategies—approaches that focus on identifying which causal pathways reliably connect an agent’s intentions to their outcomes. Additionally, I compare two BCIs that differ in their sources of occasional malfunction. I argue that the presence of causal deviance in a given case depends on the boundaries of the system that enables action. Such boundary analysis is unnecessary for bodily movements; however, for basic actions performed through a machine, it becomes essential.