v4.2.0 — What-If Analysis

What's new in v4.2.0

Answer hypotheticals without running experiments. Turing now predicts outcomes from existing data — scaling laws, ablation results, sensitivity curves, ensemble correlations — and tells you which experiments are worth running before you spend the compute.

/turing:whatif — Counterfactual Experiment Simulation

Ask "what if I had 2x more data?" or "what if I removed class 3?" and get an estimate with confidence. Routes questions to 7 estimators automatically: scaling law extrapolation, ablation study data, sensitivity interpolation, ensemble correlation, pruning sweep interpolation, pipeline stitch estimation, and budget allocation. Each answer includes confidence level (HIGH/MED/LOW) and the command to verify.

/turing:counterfactual — Input-Level Counterfactual Explanations

For a given prediction, find the smallest input change that would flip the outcome. Two methods: greedy feature perturbation (change one feature at a time) and prototype-based search (find nearest training sample from the target class). Handles numeric and categorical features, supports batch mode for all misclassified samples. Useful for debugging individual predictions and regulatory explanations.

/turing:simulate — Experiment Outcome Prediction

Before running a sweep of N experiments, predict the likely outcome distribution. Builds a weighted k-NN surrogate model from experiment history, applies a novelty penalty for configs far from the training distribution, and ranks proposed configs by predicted metric. Auto-filters: only queue experiments predicted to beat the current best. Budget savings typically >50%.

Integration

• What-if and simulation results integrated into /turing:brief research briefing
• All three commands registered in router, installer, verifier, and scaffold

Numbers

Metric	v4.1.0	v4.2.0	Delta
Tests	1576	1740	+164
Commands	66	69	+3
Scripts	85	88	+3

Two phases remaining: 28 (Model Lifecycle), 29 (Operational Intelligence).