Understanding Causation: Regularity and Counterfactual Approaches
The Regularity Approach to Causation
The regularity approach to causation focuses on broad principles and theories about causation as we understand it in objects. It separates what we can know from what is actually the case.
To begin understanding the regularity approach, we must consider the idea of causation and its origins. Epiphenomenal structures represent and capture the asymmetry of the causal relationship, doing justice to the complexity of causal structures. Therefore, the regularity approach assumes a relationship between conditions or variables because it is difficult to identify the actual impetus in a singular case, as effects are often associated with probability.
The core idea is that pinpointing a single cause for a case is challenging. Analysis must include limitations and use probability to determine causation. As Henius states, it comes back to association. Scenarios like the burning wood example highlight this, as multiple things could cause a fire. Determining a cause using frequency tables doesn’t reveal the actual cause but rather the probable cause based on the correlation’s closeness.
INUS Conditions
The concept of INUS conditions (Insufficient but Necessary parts of a condition which is itself Unnecessary but Sufficient for the effect) provides a necessary condition for symmetrical relationships. However, it doesn’t rule out situations like common cause and accidental regularities where no causal relationship exists.
For example, consider a house burning down due to a short circuit. The collection of events – the short circuit, the proximity of flammable material, and the absence of firefighters – together are unnecessary but sufficient for the house burning down. Within this collection, the short circuit is an insufficient but non-redundant part of a condition that is itself unnecessary but sufficient for the effect. Therefore, the short circuit is an INUS condition for the house burning down.
The Counterfactual Approach to Causation
Humean versions of the regularity approach were expanded by the counterfactual approach, another way to view causality. Hume stated that an object is the cause of another when “if the 1st object had not been, the 2nd never had existed” (1975 ed., p.146). David Lewis articulated this view into a theory of causality, defining it in terms of the counterfactual dependence of the effect on the cause.
For instance, saying that the short-circuit caused the fire means that if the short-circuit had not happened, the fire would not have ensued. Lewis defined causality by reference to a causal chain of counterfactually dependent events, where a sequence of events (c, e, e, …) is a chain of counterfactual dependence if and only if e counterfactually depends on c, e counterfactually depends on e, and so on. This move emphasizes that causation is a transitive relation among events.
Statements like “if c had happened, then e would have happened” are called counterfactual conditionals because they state what could or could not have happened under certain circumstances. However, specifying the conditions under which counterfactual conditionals are true or false has been notoriously difficult. Lewis articulated a complex logic of counterfactual conditionals based on the idea that besides the actual world, there are other possible worlds that can be deemed more or less similar to the actual world.
The Pairing Problem and Preemption
The pairing problem refers to the challenge of knowing which two events to focus on when analyzing causation. For example, how do we decide which x to compare with y?
Preemption is a major problem related to the manipulation approach. It means there is a cause between events where the “what would have been the cause” goes back to the cause for the original cause. For example, poison could be an intervention. If an individual places poison in a jar and a person walking in the desert dies after drinking from it, it might seem like the poison caused the death. However, someone could have pre-empted this by poking a hole in the jar, causing the death and pre-empting the poison as the cause.
Mechanisms and Determinism
Brady argues that the best approach to resolving these problems is through identifying mechanisms, as they show the chain of events that happened and have support. He believes people get stuck in their ways and research “pockets” without identifying how the cause happened or the processes involved. This prevents us from identifying the actual causes (probability).
According to Brady, we need to focus on the cause in a deterministic way, not through guesswork and probability. A chain of events leads to determinism. He highlights how probabilities tend to hide causes, and there must be a cause where probability only makes us think what it is. An experimental approach requires a solution.