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Effective attribution of causes to outcomes is crucial for optimizing complex supply chain operations. Traditional methods, often relying on waterfall logic or correlational analysis, frequently fall short in identifying the true drivers of performance issues. This paper proposes a comprehensive framework leveraging data-driven causal discovery to construct and validate Structural Causal Models (SCMs).
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AI and Deep Learning methods have revolutionized many forecasting applications but have not achieved widespread adoption in industry for aggregate forecasting. This paper challenges the AI research community by identifying three critical capabilities that current AI approaches lack: (1) multivariate consistency at scale, (2) explainable and controllable longrun assumptions, and (3) flexible incorporation
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International Journal of Research in Marketing2025In 2020, Amazon launched the Climate Pledge Friendly (CPF) program to make it easy for customers to discover and shop for products with sustainability certifications. In this paper, we measure the causal impact of products qualifying for CPF on consumer purchase behavior. Using a dataset of about 45,000 products spanning three categories, and a Differencein-Differences identification strategy, we show that
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AAAI 2025 Workshop on AI for Social Impact2025To the best of our knowledge, this work introduces the first framework for clustering longitudinal data by leveraging time-dependent causal representation learning. Clustering longitudinal data has gained significant attention across various fields, yet traditional methods often overlook the causal structures underlying observed patterns. Understanding how covariates influence outcomes is critical for policymakers
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT), NeurIPS 20242024This paper introduces the confounded pure exploration transductive linear bandit (CPET-LB) problem. As a motivating example, often online services cannot directly assign users to specific control or treatment experiences either for business or practical reasons. In these settings, naively comparing treatment and control groups that may result from self-selection can lead to biased estimates of underlying
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Credits: GENEVIEVE SHIFFRAR (Card); Stanford University (Imbens)October 13, 2021Amazon Scholar David Card wins half the award, while academic research consultant Guido Imbens shares in the other half. -
September 1, 2021Amazon’s scientists have developed a variety of scientific models to help customers get the most out of their membership.
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Glynis CondonJuly 26, 2021In a paper published at INFORMS in 2020, the Amazon senior principal scientist and his co-authors factored in both revenue and "the expected utility to the customer from the purchase."
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July 20, 2021The senior economist knows what it means to pursue a career path like hers, and she’s determined to help others along the way.
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Courtesy of Muthoni NgatiaMay 3, 2021The Amazon economist says lessons from her mother taught her a lot about how the world works, and why economics plays such a vital role.
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Credit: Yale/UPennApril 28, 2021Yale economics professor Dirk Bergemann elected to American Academy of Arts & Sciences; University of Pennsylvania computer science professor Michael Kearns elected to National Academy of Sciences.
