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Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

Tianxi Li
Haifeng Xu
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022) (2022)


Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central problem for advertisers in advertising systems. In this paper, we investigate the problem about how the advertiser can decide the bid through learning conversion incrementality, in an online manner. We formulate this problem as an episodic Markov Decision Process (MDP) and propose a novel reinforcement learning (RL) algorithm that can achieve regret at most $O(H^2\sqrt{T})$, which doesn't rely on the number of actions (bids), where $H$ is the number of rounds in each episode and $T$ is the total number of episodes in MDP. In sharp contrast with standard RL framework, conversion incrementality feedback are delayed and mixed. To handle this difficulty we propose a novel pairwise moment-matching algorithm to learn conversion incrementality, which is of independent of interest.