Why Represent Causal Information?

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This talk considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. The importance of causal cognition, I will argue, lies in particular in the special inferential role it reserves for information about underlying mechanisms.