Lossy Compression and the Granularity of Causal Representation

A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from information theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness. We then show, across three studies (N=1,391), that participants’ choices over causal models demonstrate a preference for more compressed causal models when all other factors are held fixed, with some further tolerance for lossy compressions.