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Despite warnings against inferring causality from observed correlations or statistical dependence, some articles do. Observed correlation is neither necessary nor sufficient to infer causality as defined by the term's everyday usage. For example, a deterministic causal process creates pseudorandom numbers; yet, we observe no correlation between the numbers. Child height correlates with spelling ability because age causes both. Moreover, order is problematic---we hear train whistles before observing trains, yet trains cause whistles. Scientific methods specifically prohibit inferring causal theories from specific observations (i.e., effects) because, in part, many credible causes are perfectly consistent with available observations. Moreover, actions inferred from effects have more unintended consequences than actions based on sound deductive causal theories because causal theories predict multiple effects. However, an often overlooked but key feature of these theories is that we describe the cause with more variables than the effect. Consequently, inductive processes might appear deductive as the number of effects increases relative to the number of potential causes. For example, in real criminal trials, jurors judge whether sufficient evidence exists to infer guilt. In contrast, determining guilt in criminal mystery novels is deductive because the number of clues (i.e., effects) is large relative to the number of potential suspects (i.e., causes). We can make inferential tasks resemble deductive tasks by increasing the number of effects (i.e., variables) relative to the number of potential causes and seeking a shared cause for all observed effects. Moreover, under some conditions, the method of seeking shared causes might approach deductive reasoning when the number of causes is strictly limited. At least, the resulting number of possible causal theories is far less than the number generated from repeated observations of a single effect (i.e., variable).