Causal Inference on Stopped Random Walks in Online Advertising

• URL: http://arxiv.org/abs/2602.05997v1
• Date: Thu, 05 Feb 2026 18:43:29 GMT
• Title: Causal Inference on Stopped Random Walks in Online Advertising
• Authors: Jia Yuan Yu,
• Abstract summary: We consider a causal inference problem in online advertising systems.<n>Each treatment corresponds to a parameter value of the advertising mechanism.<n>We estimate through experiments the corresponding long-term treatment effect.
• Score: 2.758187691493102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a causal inference problem frequently encountered in online advertising systems, where a publisher (e.g., Instagram, TikTok) interacts repeatedly with human users and advertisers by sporadically displaying to each user an advertisement selected through an auction. Each treatment corresponds to a parameter value of the advertising mechanism (e.g., auction reserve-price), and we want to estimate through experiments the corresponding long-term treatment effect (e.g., annual advertising revenue). In our setting, the treatment affects not only the instantaneous revenue from showing an ad, but also changes each user's interaction-trajectory, and each advertiser's bidding policy -- as the latter is constrained by a finite budget. In particular, each a treatment may even affect the size of the population, since users interact longer with a tolerable advertising mechanism. We drop the classical i.i.d. assumption and model the experiment measurements (e.g., advertising revenue) as a stopped random walk, and use a budget-splitting experimental design, the Anscombe Theorem, a Wald-like equation, and a Central Limit Theorem to construct confidence intervals for the long-term treatment effect.

Related papers

• Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models [11.13292106104811]
  Existing scheduling policies rely on simples, such as uniform spacing and frequency caps, that overlook long-term user interest.<n>We model change in user interest upon showing ads based on mere exposure, hedonic adaptation, and operant conditioning.<n>We show that, if the number of displayed ads is fixed, then the optimal ad-schedule only depends on the operant conditioning function.
  arXiv  Detail & Related papers  (2025-09-24T16:35:18Z)
• Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy [33.14965788272627]
  We introduce AdsBPC, a novel user-level differential privacy protection scheme for online advertising measurement results.<n>AdsBPC achieves a 33% to 95% increase in accuracy over existing streaming DP mechanisms applied to advertising measurement.
  arXiv  Detail & Related papers  (2024-06-04T16:31:19Z)
• Online Ad Procurement in Non-stationary Autobidding Worlds [10.871587311621974]
  We introduce a primal-dual algorithm for online decision making with multi-dimension decision variables, bandit feedback and long-term uncertain constraints. We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are adversarial, adversarially corrupted, periodic, and ergodic.
  arXiv  Detail & Related papers  (2023-07-10T00:41:08Z)
• Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics [53.62091043347035]
  We study a game between autobidding algorithms that compete in an online advertising platform.<n>We propose a gradient-based learning algorithm that is guaranteed to satisfy all constraints and achieves vanishing individual regret.
  arXiv  Detail & Related papers  (2023-01-30T21:59:30Z)
• Fair Effect Attribution in Parallel Online Experiments [57.13281584606437]
  A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly. Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes.
  arXiv  Detail & Related papers  (2022-10-15T17:15:51Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Causal Inference in the Presence of Interference in Sponsored Search Advertising [11.514573594428352]
  In this paper, we leverage the language of causal inference in the presence of interference to model interactions among the ads. We illustrate the utility of our formalization through experiments carried out on the ad placement system of the Bing search engine.
  arXiv  Detail & Related papers  (2020-10-15T01:13:14Z)
• An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis [67.58267006314415]
  We present a large-scale analysis on ad-related user feedback from App Store and Google Play. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage. Some ad issue types are addressed more quickly by developers than other ad issues.
  arXiv  Detail & Related papers  (2020-08-22T05:38:24Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers Willingness to Pay [79.9312329825761]
  We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand. Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
  arXiv  Detail & Related papers  (2020-05-14T09:26:57Z)
• Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns [115.96295568115251]
  We study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. For every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT) We present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.
  arXiv  Detail & Related papers  (2020-03-03T11:07:38Z)
• Online Causal Inference for Advertising in Real-Time Bidding Auctions [1.9336815376402723]
  This paper proposes a new approach to perform causal inference on advertising bought through real-time bidding systems. We first show that the effects of advertising are identified by the optimal bids. We introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem.
  arXiv  Detail & Related papers  (2019-08-22T21:13:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.