Temporal credit assignment (TCA) concerns how past actions causally influence future outcomes. Hindsight credit assignment (HCA) addresses this by conditioning on future states, but typically treats those states as monolithic and obscuring the causal structure among their factors. In complex dynamical systems, credit may arise from either pairwise or higher-order dependencies between factors, including synergistic effects that emerge only jointly and redundant effects shared across correlated factors. In this work, we argue that reliable credit assignment requires explicitly identifying these pairwise and higher-order factor dependencies. We introduce factorized HCA, which decomposes credit over factor connections and uses information-theoretic measures to identify pairwise, synergistic, and redundant interactions. On controlled benchmarks, pairwise methods succeed when dependencies are direct but fail under synergy and redundancy, whereas higher-order conditioning recovers the correct credit signal.