What Accurate Predictions Hide: Tracing Action-Effect Errors in World Models via Paired Rollouts

World models support planning and policy learning by predicting how actions change future outcomes, yet accurate predictions of the usual outcome can conceal errors in action dependence. We use paired rollouts to compare how alternative action sequences change environment and model outcomes from a shared history. Because each generated step conditions the next, we use differential transport to decompose action-effect error exactly into local discrepancies propagated by later model dynamics, measuring their contributions through controlled transition substitutions. Across ten Craftax world models, cancellation and delay reveal outcomes predicted without the required action or before its execution. Substantial action-effect errors persist in about 13% of assessments passing per-branch return checks. Supervising next-step outcomes of alternative actions raises the rate of correctly timed event predictions under delay by over 40 percentage points relative to matched replay on average. Controlled substitutions show that using the updated generator for only the first transition suppresses later false events across three training repetitions of a shared pretrained model, while correct event generation changes little and remains rare. These findings make action dependence a concrete target for evaluation and training, with controlled substitutions revealing how local model updates change delayed consequences.