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KDD 2025 Workshop on AI for Supply Chain2025AI 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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NeurIPS 2025 Workshop on Uncovering Causality in Science2025Online randomized controlled experiments (A/B tests) measure causal changes in industry. While these experiments use incremental changes to minimize disruption, they often yield statistically insignificant results due to low signal-to-noise ratios. Precision improvement (or reducing standard error) traditionally focuses on trigger observations - where treatment and control outputs differ. Though effective
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Journal of the Royal Statistical Society, Series B2025Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we derive the finite-sample properties
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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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