inverse-propensity-scoring

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bagheri365 / PolicyRecLab

A research lab for off-policy evaluation, exploration, and policy-generated bias in contextual-bandit recommendation systems.

recommender-systems causal-inference importance-sampling contextual-bandits exploration-exploitation selection-bias counterfactual-learning off-policy-evaluation doubly-robust inverse-propensity-scoring

Updated Sep 9, 2026
Python

lavanyag-wq / Reco-loop-collapse

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Closed-loop recommender lab: a model retrains on its own exposure while an exact simulated answer key watches. Logged CTR rose 145.1% while 15 of 16 segments stayed locked on the wrong item; the naive offline reading of a challenger overstated it by 688%, IPS rested on an ESS of 2 of 20000, and the tool's LOW_ESS exit code refuses both.

python machine-learning simulation exploration reproducibility recommender-systems counterfactual feedback-loops mlops off-policy-evaluation popularity-bias inverse-propensity-scoring

Updated Oct 7, 2026
Python

srujan20 / offpolicy-gap

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Off policy evaluation audit for logged bandits: measures how much of a target policy's probability mass sits outside what the log could have produced, shows that a 95 percent interval around the standard estimator covers 0.26 of the time, and returns exit 2 rather than a number when the estimate would be about a different quantity.

python numpy ci-cd recommender-systems causal-inference model-validation importance-sampling contextual-bandits mlops github-actions off-policy-evaluation doubly-robust ml-evaluation counterfactual-evaluation inverse-propensity-scoring

Updated Aug 26, 2026
Python

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