Abhimanyu Mukerji, Sushant More, Ashwin Viswanathan Kannan, Lakshmi Ravi, Hua Chen, Naman Kohli, Chris Khawand, Dinesh Mandalapu
arXiv 21 Aug 2024 · Machine Learning
arXiv:2408.11967 · PDF · DOI · OpenAlex · Extracted main text
With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | More, S., Kotwal, P., Chappidi, S., Mandalapu, D., Khawand, C (2023) Double Machine Learning at Scale to Predict Causal Impact of Customer Actions self | 0.511 | 2 | 1 | 100% |
| 2 | Young, Scott W. H (2014) "Improving Library User Experience with A/B Testing: Principles and Process" | 0.405 | 1 | 1 | 100% |
| 3 | AWSlambda https://aws.amazon.com/lambda/aws.amazon.com/lambda/ | 0.405 | 1 | 1 | 100% |
| 4 | Chen, H., Harinen, T., Lee, J. Y., Yung, M., and Zhao, Z (2020) CausalML: Python package for causal machine learning self | 0.405 | 1 | 1 | 100% |
| 5 | Syrgkanis, V., Lewis, G., Oprescu, M., Hei, M., Battocchi, K., Dillo… (2021) Causal inference and machine learning in practice with EconML and CausalML: Industrial use cases at Microsoft, TripAdvisor, Uber self | 0.405 | 1 | 1 | 100% |
| 6 | Hartford, J., Lewis, G., Leyton-Brown, K., and Taddy, M (2017) Deep IV: A flexible approach for counterfactual prediction | 0.405 | 1 | 1 | 100% |
| 7 | Athey, S., and Wager, S (2021) Policy learning with observational data | 0.405 | 1 | 1 | 100% |
| 8 | Sekhon, Jasjeet (2007) The Neyman–Rubin Model of Causal Inference and Estimation via Matching Methods | 0.405 | 1 | 1 | 100% |
| 9 | Quarterly Retail E-Commerce Sales Report, U.S. Census Bureau | 0.405 | 1 | 1 | 100% |
| 10 | Sharma, A. and Kiciman, E (2020) DoWhy: An end-to-end library for causal inference | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.