Although we live in a complex and multi-causal world, learners often lack sufficient data and/or cognitive resources to acquire a fully veridical causal model. The general goal of making precise predictions with energy-efficient representations suggests a gen- eric prior favoring causal models that include a relatively small number of strong causes. Such ‘‘sparse and strong” priors make it possible to quickly identify the most potent individual causes, rel- egating weaker causes to secondary status or eliminating them from consideration altogether. Sparse-and-strong priors predict that competition will be observed between candidate causes of the same polarity (i.e., generative or else preventive) even if they occur independently. For instance, the strength of a moderately strong cause should be underestimated when an uncorrelated strong cause also occurs in the general learning environment, rela- tive to when a weaker cause also occurs. We report three experi- ments investigating whether independently-occurring causes (either generative or preventive) compete when people make judg- ments of causal strength. Cue competition was indeed observed for both generative and preventive causes. The data were used to assess alternative computational models of human learning in complex multi-causal situations.