arikw/intervention-learning-framework
intervention-learning-framework (v1, milestone 1) A recursive intervention-learning framework for a real-time sales-call assistant, built simulator-first: every estimator is validated by recovering known ground truth from the generative simulator in intervene/sim/. No production data exists yet; nothing in this repo claims a result from real data, and no estimate is reported without an uncertainty interval. Milestone 1 scope: simulator + detection + offline effect estimation… See the full description on the dataset page: https://huggingface.co/datasets/arikw/intervention-learning-framework.
add decision 16 (conditional baseline ground truth)
re-push: force remount of truncated file
fix precision_emp: P(label=1 | fired), not firing rate
fix flat_labels layout to moment-major; add conditional baseline ground truth
fix: conditional baseline truth + decisions_df drop semantics
fix cost threshold: per-candidate expected cost, matching docstring and brute force
experiment script: pass objective lag to planning gate
fix: pass objective resolution lag to plan()
fix: DatetimeIndex comparison returns ndarray, not Series
fix positivity test: refusal is at p=0/1 exactly
README: layout, estimator assumptions and failure modes
DECISIONS.md
reproducible milestone-1 experiment script
simulator parameter-recovery tests
detection tests: calibration, PR curves, cost threshold
headline confounding parameter-recovery test
planning calculator tests
domain model contract tests
fix numpy import in planning
packaging + pytest config
fix moment-budget validator: expected occurrence mass via quadrature
test fixtures: milestone-1 simulator config
monitors skeleton (milestone 2)
per-moment effect table helper
power/planning calculator (implemented in v1)
fix broken both-arms check in ipw
OPE layer skeleton (milestone 2)
policy layer skeleton (milestone 2)
discovery loop skeleton + CandidateMoment model
generator cleanup: confidence vectorised, no placeholder
calibrated moment detector
effects re-exports
pooled/stratified/IPW estimators with CIs
detect re-exports
calibration, ECE, PR curves, cost threshold
config: clean numpy import
generative simulator + analytic ground truth
versioned pydantic domain models
domain re-exports
sim re-exports
simulator config
package init
initial commit