The Relations Between Causal (x2) and Counterfactual Reasoning, the Hindsight Bias and Regret (and the kitchen sink)

The research areas of causal and counterfactual reasoning, hindsight bias and regret, have often been studied in isolation, sometimes studied in pairs, and occasionally studied in triads. I suggest that there are common mechanisms shared by these judgments that explain how, when, and why they will (a) be similarly or differently affected by information and (b) influence each other. To start, I distinguish two types of causal reasoning: the types of judgments we make in science when we have multiple examples of causes and effect and the types of judgments we make in law when we want to figure out the cause of a one-time only event. In the former, an important cue to causality is covariation -a cause is something that increases the probability of an effect above its usual probability. I then draw an analogy to the latter -and assume that a causality judgment about a person or event is a function of how much that person or event increases the probability of the eventual outcome above its "baseline" probability (i.e., its natural probability of occurring). C ≈ p(Oafter) − p(Obefore ) The equation above represents how a causality judgment is a function of the estimated probability of the eventual outcome occurring after the target cause has occurred [p(Oafter)] and the estimated probability of the eventual outcome occurring before the target cause has occurred [p(Obefore)]. But for one-time events, how can people make probability judgments? I suggest that such judgments rely on preexisting knowledge -especially of previous covariations and causal mechanisms -and counterfactual reasoning. The equation below expands the one above by putting each estimate over 1 (i.e., p(Oafter) + p(~Oafter) = 1).