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Found 14 papers, 4 papers with code
Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data
Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples.
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting
We consider learning causal relationships under conditional moment restrictions.
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting, using a conditional density ratio estimator.
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Based on the properties of the evaluation policy, we categorize OPE situations.
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
Learning Classifiers under Delayed Feedback with a Time Window Assumption
We consider training a binary classifier under delayed feedback (\emph{DF learning}).
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using a nonparametric estimator of the density ratio between the historical and evaluation data distributions.
Debiased Off-Policy Evaluation for Recommendation Systems
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems.
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
We prove its consistency for the feedback shift.
Dual Learning Algorithm for Delayed Conversions
To overcome these challenges, we propose two unbiased estimators: one for CVR prediction and the other for bias estimation.
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
We study the model selection problem in conditional average treatment effect (CATE) prediction.
Fatigue-Aware Ad Creative Selection
In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement).
Efficient Counterfactual Learning from Bandit Feedback
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback?
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