usaito/CFML-papers

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usaito/CFML-papers

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Summaries and notes on CounterFactual Machine Learning papers

recsys2021-tutorial

https://sites.google.com/cornell.edu/recsys2021tutorial

Jupyter Notebook

counterfactual-cv

(ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’

unbiased-implicit-rec-real

(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

unbiased-implicit-rec

(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

dr-ranking-metric

(RecSys2020) "Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions"

asymmetric-tri-rec-real

(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

icml2022-mips

(ICML2022) Off-Policy Evaluation for Large Action Spaces via Embeddings

unbiased-pairwise-rec

(ICTIR2020) "Unbiased Pairwise Learning from Biased Implicit Feedback"

kdd2022-tutorial

Jupyter Notebook

ijcai2022-adversarial-mf

Towards Resolving Propensity Contradiction in Offline Recommender Learning

icml2023-offcem

(ICML2023) Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling

kdd2022-fair-ranking-nsw

(KDD2022) Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking.

Jupyter Notebook

www2024-lope

(TheWebConf'24) Long-term Off-Policy Evaluation and Learning

Jupyter Notebook

obp-demo