Optimal Causal Inference

par/by Susanna Still
Department of Information and Computer Sciences
University of Hawaii

I will talk about how theory building can naturally distinguish between regularity and randomness. Starting from basic modeling principles I will argue for a general information-theoretic objective function that embodies a trade-off between a modelĒs complexity and its predictive power. The family of solutions derived from this principle corresponds to a hierarchy of models. At each level of complexity, those models achieve maximal predictive power, and in the limit of optimal prediction a processĒ exact causal organization is identified. Examples show how theory building can profit from analyzing a processĒ causal compressibility, which is reflected in the optimal modelsĒ rate-distortion curve.