Causal reasoning — the ability to understand and reason about cause-and-effect relationships — is fundamental to prediction, explanation, planning, and learning. Humans are inveterate causal thinkers: we seek explanations for events, predict consequences of actions, and attribute blame and credit. Understanding how people infer, represent, and reason about causality is central to cognitive psychology.
Detecting Causes
How do we determine that X causes Y? Multiple cues contribute. Temporal priority (causes precede effects), covariation (causes and effects co-occur), spatial and temporal contiguity (causes and effects are close in space and time), and mechanism information (understanding how X could produce Y) all inform causal judgments. However, people often over-rely on covariation and temporal contiguity, leading to spurious causal beliefs (e.g., superstitions arising from coincidental correlations).
Causal Models
Judea Pearl and others have developed causal Bayesian networks as formal representations of causal knowledge. These directed graphical models represent variables as nodes and causal relationships as directed edges, capturing both the structure of causal relationships and the strength of causal influences. Experimental evidence suggests that people's causal reasoning is well described by causal Bayesian networks in many situations, though systematic biases (such as the Markov violation — sensitivity to distal causes that should be screened off by proximal ones) also occur.
Infants as young as 6 months show sensitivity to causal relationships. In launching events (one ball strikes another, which then moves), infants expect the second ball to move only after being contacted, expect the direction and speed to be consistent with the collision, and show surprise when spatial or temporal gaps violate causal continuity. By 2 years, children actively explore objects to determine their causal properties, demonstrating that causal reasoning is an early-developing cognitive capacity.