-
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
-
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
-
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
-
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
-
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
Related content
-
February 28, 2023How the former astrobiology professor is charting new territory as a scientist for Amazon Flex.
-
February 8, 2023How her background helps her manage a team charged with assisting internal partners to answer questions about the economic impacts of their decisions.
-
December 9, 2022Amazon provided funding for two-week workshop led by Nobel Prize winner Thomas Sargent.
-
October 17, 2022Tatevik Sekhposyan, Amazon Scholar and Texas A&M University professor, enjoys the flexibility of economics and how embracing uncertainty can enhance prediction.
-
September 13, 2022Paper introduces a unified view of the learning-to-bid problem and presents AuctionGym, a simulation environment that enables reproducible validation of new solutions.
-
August 5, 2022How the Amazon Supply Chain Optimization Technologies principal economist uses his expertise in time series econometrics to forecast aggregate demand.