I’ve been mapping out the statistical signatures that precede self‑sabotaging choices—think sudden spikes in cortisol, specific word‑use patterns, or even subtle changes in sleep cycles. Maybe we could cross‑reference those markers with the real‑world decisions people make and see if there’s a predictive model that actually works.

Right, so define self‑sabotage operationally first—maybe a composite score from behavioral flags and self‑reports. Then pull in the physiological, contextual, and psychological variables, standardize them, and train a cross‑validated model. Keep a holdout set and maybe use permutation tests to confirm that any predictive power isn’t just noise. I’ll also set up a causal inference framework, like instrumental variables or difference‑in‑differences, to tease out causation. I’ll remember that even the cleanest numbers still need a human story behind them.

Got it—I'll add a latent variable model to capture the unreported sabotage and use a robust feature‑selection step to guard against overfitting. For the IV, I’ll look into exogenous policy changes or random assignment of counseling groups as potential instruments. And I'll keep a SHAP plot or partial dependence for interpretability. That way the numbers stay transparent and the story stays human.

Thanks, that’s the plan—stay skeptical, keep the assumptions in check, and let the data point the way, not dictate the story.