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This paper investigates regret minimization in repeated first-price auctions (FPAs) by proposing algorithms that jointly estimate private values and optimize bidding strategies based on observed outcomes. It addresses the challenges of estimating the value of an impression, which is determined by the difference between outcomes of winning and losing, and introduces three models for potential outcomes. The framework eliminates the need for the overlap condition in causal inference, providing algorithms that

Author

shriprathaa

Language

EN