New paper in JAMA that introduces causal graphs; I saw it mentioned by @sander on twitter.
Lipsky, A. M., & Greenland, S. (2022). Causal Directed Acyclic Graphs. JAMA. link
A more fundamental intro on algorithmic information theory by Nick Szabo that is worth preserving can be found below. His main scholarship is far afield from medical research, but much closer to another interest of mine – monetary economics and insurance.
Causal inference is directly linked to algorithmic information theory via the concept of “minimum description length.” A correct causal model will produce an estimate of future data (assuming sample size is adequate) better than any model that omits the causal factor. It will also balance parsimony (descriptive complexity) with predictive accuracy. Proceeding as Geisser recommends above (and building upon JL Kelly’s paper A New Interpretation of the Information Rate (pdf), those who understand causal factors will win bets against those who do not.
Nick Szabo (1996) Introduction to Algorithmic Information Theory
Blockquote
The combinations that can arise in a long series of coin flips can be divided into regular sequences, which are highly improbable, and irregular sequences, which are vastly more numerous. Wherever we see symmetry or regularity, we seek a cause. Compressibility implies causation.