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KDD 2023 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2023This paper proposes a learning model of online ad auctions that allows for the following four key realistic characteristics of contemporary online auctions: (1) ad slots can have different values and click-through rates depending on users’ search queries, (2) the number and identity of competing advertisers are unobserved and change with each auction, (3) advertisers only receive partial, aggregated feedback
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KDD 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision (CDPD)2023Companies offering web services routinely run randomized online experiments to estimate the “causal impact” associated with the adoption of new features and policies on key performance metrics of interest. These experiments are used to estimate a variety of effects: the increase in click rate due to the repositioning of a banner, the impact on subscription rate as a consequence of a discount or special
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ECML PKDD 20232023Causal Impact (CI) measurement is broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate average and conditional average treatment effects across 100s of customer action types for e-commerce and digital businesses and 100s of millions of customers that can be used in decisions
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ASEE 20232023The majority of students who choose to major in engineering do so to become a part of the community of practice of professional engineers (Johri & Olds, 2011), meaning that they want to have adequate exposure to what a career as a professional engineer could potentially be as part of their college experience. However, according to Jonassen (2014), engineering graduates are not well trained to contribute
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023Network interference, where observed outcomes are influenced by interaction with nearby units, is a fundamental issue in A/B testing and experimentation in social and economic networks. Clustered randomization is a frequently-used strategy that aims to prevent confounding by limiting interaction between treated and untreated units. We study a model of least-squares estimation under network interference,
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