Anytime-Valid Confidence Sequences for Multi-Treatment Messaging
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Solution Overview
Problem
Current methods for confidence sequencing in A/B testing of messaging treatments are error-prone, computationally burdensome, and restrictive when testing multiple treatments simultaneously, leading to inaccurate results and high latency, especially as the number of comparisons increases.
Innovation Solution
The method involves calculating anytime-valid confidence sequences by estimating the variance of the average treatment effect, using error-corrected p-values within confidence bounds to control type I error, and dynamically updating confidence values in real-time, allowing for accurate and efficient evaluation of multiple messaging treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple messaging treatments are tested simultaneously using traditional A/B testing methods, then the ability to compare multiple treatments is improved, but the error rate increases and computational burden increases
Solution Approach 1:
The patent segments the testing process into independent confidence sequence calculations for each treatment pair, where each sequence is computed separately and then combined through multiple testing corrections. This allows simultaneous testing of multiple treatments while maintaining statistical validity by treating each comparison as a distinct unit that can be independently analyzed and then aggregated with appropriate error control.
Solution Approach 2:
The patent introduces confidence sequences as an intermediary statistical construct that bridges the gap between multiple treatment comparisons and valid inference. These sequences serve as a mediator that controls type I error accumulation across multiple tests while enabling continuous monitoring, thus allowing versatile multi-treatment testing without sacrificing reliability.
2Adaptability or versatility
If traditional A/B testing methods are used for multiple treatments, then comprehensive comparison is improved, but computational complexity and latency increase
Solution Approach 1:
The patent performs preliminary calculations by pre-computing confidence sequences for each treatment pair independently before combining them. This preliminary action allows the system to prepare statistical bounds in advance, reducing the computational complexity during the actual multi-treatment comparison phase and enabling efficient real-time analysis.
Solution Approach 2:
The patent employs dynamic confidence sequences that can be continuously updated as new data arrives, rather than requiring fixed-sample-size tests. This dynamic approach allows the system to adaptively monitor multiple treatments in real-time with reduced computational burden, as the sequences can be updated incrementally without re-computing entire test statistics.
3Productivity
If continuous monitoring of multiple treatments is performed, then real-time results are improved, but type I error accumulation increases
Solution Approach 1:
The patent implements feedback control through confidence sequences that continuously monitor treatment effects while automatically adjusting for multiple comparisons. The sequences provide real-time feedback on statistical significance while incorporating error control mechanisms that prevent type I error accumulation, allowing productive continuous monitoring without sacrificing reliability.
Solution Approach 2:
The patent changes the statistical parameter from fixed p-value thresholds to time-varying confidence sequences that adapt to the number of comparisons performed. This parameter transformation allows real-time monitoring by converting the error control problem into a sequence of bounded confidence intervals, maintaining reliability while enabling continuous productivity.
Data Source
AI summary
Certain aspects and features of this disclosure relate to providing anytime-valid confidence sequences for multiple messaging treatments in an experiment. A process controls and/or corrects statistical error when multiple messaging treatments are being evaluated together. Messages can be stored, formatted, and transmitted from a communication server or other computing system. In one example, each test message from among multiple test messages is sent to an independent group of recipients over some period of time. An analytics application programmatically evaluates a metric related to message responses over time and determines a difference in the metric for each of several unique messages as compared to a baseline message. The analytics application also determines a confidence value and can display the changing confidence value in sequence over time along with the current difference, or lift, while maintaining the accuracy of the values.