What is it about?
Companies often introduce new products or features gradually, giving access to different groups of customers over time. When these rollouts happen during periods of rapidly changing customer behavior, such as the holiday shopping season, it becomes difficult to tell whether changes in business performance are caused by the new feature or by the season itself. In this paper, we present and deploy a statistical pipeline that combines modern causal inference methods to separate these effects. Using data from Affirm’s Buy Now, Pay Later platform, we show that the approach correctly identifies the impact of a real product rollout, while simpler methods would have reached the wrong conclusion.
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Why is it important?
Organizations increasingly rely on controlled rollouts to evaluate new products, pricing strategies, and customer experiences. Yet many of the most important launches happen during periods of intense seasonality, such as holiday shopping or major promotional events, when traditional evaluation methods become unreliable. Our work provides a practical, deployed framework for estimating causal effects under these challenging conditions. Rather than introducing another statistical estimator, we show how existing state of the art methods can be combined into a robust pipeline that practitioners can apply to real-world product launches across industries such as retail, finance, travel, and digital platforms.
Perspectives
What I enjoyed most about this project was showing that rigorous causal inference can solve practical business problems, not just theoretical ones. The challenge wasn’t inventing a new statistical estimator, it was figuring out how to combine the right tools into a workflow that could be deployed in a real production environment and produce trustworthy answers when simpler analyses failed. Working with Julia Yi made that possible, and I hope this paper encourages more practitioners to think carefully about how they evaluate product launches, especially in complex, highly seasonal settings. Good decisions depend on good measurement.
Andres Arcila
University of Waterloo
Read the Original
This page is a summary of: A Deployed Cohort-Aware Counterfactual Imputation Pipeline for Measuring Staggered Rollouts Under Extreme Seasonality: Evidence from Affirm's BNPL Platform, August 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3770855.3818310.
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