Feature engineering has always had a faint smell of kitchen experimentation. Take the raw variables. Add ratios. Try logs. Multiply this by that. Remove the ones that look useless. Feed everything into XGBoost. Pretend the process was scientific because the final notebook has a clean cross-validation table. In many business analytics teams, this is not a caricature. It is Tuesday. ...
Maps are useful because they are not the territory. Nobody opens Google Maps and assumes the blue line has physically repaired the road. Sensible people use it to orient themselves, notice routes, avoid obvious mistakes, and decide where to inspect more carefully. That is the cleanest way to read DEMOCRITUS, the system described in Large Causal Models from Large Language Models.1 It does not make LLMs magically perform causal inference. It does not estimate treatment effects. It does not solve confounding. It does not turn a pile of text into scientific truth by sprinkling geometry on top, though that would be a very efficient way to sell consulting decks to executives with poor impulse control. ...
A hospital monitor, a factory sensor array, and a trading dashboard have a shared irritation: they all produce time-series data that everyone wants to model, almost nobody wants to share, and absolutely nobody fully understands from correlations alone. That is the practical problem behind KarmaTS, a proposed interactive framework for constructing executable, lag-indexed causal simulations for multivariate time series.1 The paper is not trying to sell another magical causal-discovery algorithm. Good. We have enough of those wandering around with heroic acronyms and very delicate assumptions. ...