Predictive-Modeling

A hospital does not buy “intelligence.” It buys a workflow. That distinction sounds obvious until an AI vendor arrives with a model that has billions of parameters, a clinical pretraining story, and the gentle implication that smaller models are now museum pieces. In the ICU, however, the useful question is not whether the model can talk like a doctor. It is whether it can detect tomorrow’s clinical deterioration from messy notes better than simpler systems that cost less, run faster, and attract fewer infrastructure headaches. ...

TL;DR for operators A portfolio does not care whether your signal has a beautiful causal origin story. It cares whether the signal points in roughly the right direction, ranks assets usefully, and is scaled well enough not to produce absurd weights. That is the useful, slightly impolite message of Alejandro Rodriguez Dominguez’s paper, Is Causality Necessary for Efficient Portfolios?1 The paper challenges a strong claim in recent causal factor-investing work: that causal factor models are necessary for investment efficiency. Its answer is narrower and more operational. Within static mean-variance and related quadratic optimisation frameworks, causal identification is not the necessary condition. The necessary operating conditions are geometric. ...