Philosophy
The Metaphysics of Causation: Counterfactual, Regularity, and Process Theories
Quick fact
David Lewis's counterfactual theory of causation defines a cause as an event that makes a difference: had the cause not occurred, the effect would not have occurred either, in the closest possible worlds. This elegantly handles many everyday causal claims but famously struggles with cases of causal overdetermination, where two independent causes are both sufficient for the effect.
Why this is interesting
We all think we know what causation is—a billiard ball hits another and it moves. But philosophers have found that 'cause' is surprisingly slippery: is it just a pattern, a counterfactual, or a physical process?
Read the full explanation
Understanding The Metaphysics of Causation: Counterfactual, Regularity, and Process Theories
Imagine you flip a light switch and the light turns on. What makes the flip the cause? One obvious answer: every time you flip the switch, the light goes on—they're constantly conjoined. This is the regularity theory: causation is just constant conjunction of events. If whenever event A happens, event B always follows, then A causes B. But is constant conjunction enough? Imagine the famous example of a rooster crowing at dawn: the crowing and the sunrise are constantly conjoined, but the rooster does not cause the sun to rise. Also, a regular pattern might be a coincidence. The counterfactual theory steps in: instead of just noticing patterns, ask 'would the effect have occurred if the cause had not?' If the switch had not been flipped, the light would not be on. This seems to capture the idea of making a difference. Philosophers like David Lewis formalized this using possible worlds: we look at the closest possible worlds where the cause does not occur, and if the effect also does not occur, then the cause is necessary for the effect. But counterfactual dependence can be tricky too. Consider a preempted cause: a rock is thrown and shatters a window, but a second rock was also thrown a second later and would have shattered it if the first hadn't. The first rock's throw is the cause, but had it not occurred, the window still would have shattered (from the second rock). The counterfactual analysis fails here. Process theories offer yet another angle: they say a causal connection is a physical process that transmits a mark or a conserved quantity, like energy or momentum. In the billiard ball example, the first ball transfers momentum to the second, so that is a causal process. This theory focuses on the actual physical interactions, avoiding the need for possible worlds and handles preemption more easily: the first ball's process is the cause, the second ball's process (which never connects) is not.
A deeper explanation
Each theory of causation highlights a different feature we care about. - Regularity theory (Hume): causation is nothing more than constant conjunction. It is empiricist and parsimonious: we can observe regularities but not 'powers'. However, it fails to distinguish genuine causes from mere regularities and can't explain why the sun rising is not caused by the rooster's crow. - Counterfactual theory (Lewis): causation is counterfactual dependence. It captures the idea that causes are necessary for their effects in ordinary circumstances. The trick is defining the 'closest possible worlds'—worlds that are as similar as possible to ours, but where the cause does not occur. This theory provides a clear semantics but struggles with overdetermination and preemption, requiring elaborate strategies like 'late preemption' and 'causal chains'. - Process theory (Salmon, Dowe): causation is physical process that transmits a conserved quantity. This theory grounds causation in actual physical interactions, avoiding modal dependence. It handles preemption and overdetermination well, because a process is either present or not. But it requires a physicalist worldview and may not apply to social or mental causation. These theories are not just abstract: they influence how we program AI to infer causes, how we understand legal responsibility, and how we interpret correlations in science. The choice of theory has consequences for what counts as a cause, so understanding their differences helps us critically evaluate causal claims.