Causal Reasoning Under Resource Constraints: Asymmetry, Search, and Cognitive Bias

Why is inferring a cause from an observed effect typically harder than predicting an effect from a known cause? We propose that backward causal inference is difficult because it requires resource-constrained search over a space of possible explanations, whereas forward projection often relies on the application of an already available internal causal model. We formalize backward reasoning as a three-phase process of generation, evaluation, and pruning over a finite hypothesis space. The efficiency of this process depends on a latent resource ratio, eta = Eᵣesp / Eₘain, which captures the balance between resources allocated to evidence-responsive processing and resources allocated to maintaining the current representational state. On this basis, we introduce a minimal search-efficiency index, S (eta, N) = (eta * log₂ N) / (1 + alpha * N), which characterizes how search performance degrades as hypothesis-space size increases and available resources decline. The framework reinterprets classic biases in causal reasoning—including undergeneration, confirmation bias, premature closure, and primacy effects—as consequences of constrained search rather than as arbitrary failures of reasoning. It further yields discriminative predictions concerning hypothesis-space complexity, resource manipulations, expertise, and the effects of external search scaffolds. More broadly, we distinguish between the rich causal structure of the environment and the simplified causal models available to the reasoner, and use this distinction to explain why backward inference often requires active search beyond direct model application. The result is a process-level account of why backward causal inference is harder than forward projection. Keywords: causal reasoning; backward inference; hypothesis-space search; resource constraints; cognitive bias