Multilayer Online Experiment Buckets for Automated Bias Detection
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Solution Overview
Problem
Existing methods for managing online experiments are inefficient and prone to human errors, leading to potential biases and contamination in experiment layers, which can significantly impact revenue and user engagement, and lack scalable solutions for detecting and preventing quality issues in large-scale online experiments.
Innovation Solution
A framework for automatically detecting and preventing quality issues in online experiments using multi-layered statistical tests, including uniformity and proportion tests, to identify and correct contaminated buckets and layers, and implementing prevention actions such as reseeding or isolating problematic segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical techniques are used to determine biases and issues with buckets, then detection capability is provided, but the process is time-consuming and has high potential for human errors
Solution Approach 1:
The patent replaces traditional manual statistical techniques with an automated system that uses computational algorithms to detect biases and issues in experiment buckets. The system automatically analyzes user event data, performs statistical tests, and identifies contaminated buckets without human intervention, thereby eliminating time-consuming manual processes while maintaining or improving detection precision.
Solution Approach 2:
The system performs self-diagnosis and self-correction by automatically detecting contaminated buckets and triggering remediation actions. The automated experimentation management system monitors its own experiment layers, identifies quality issues, and executes correction protocols without requiring external human analysis, enabling the system to serve itself in maintaining experiment integrity.
2Reliability
If traditional testing and debugging techniques are applied, then some quality issues can be identified, but the process is not scalable when there are billions of live variants
Solution Approach 1:
The patent creates a universal experimentation management system that can handle any number of experiment variants through standardized automated processes. The system uses a common framework that works across billions of live variants, performing consistent statistical analysis and contamination detection regardless of scale, thereby achieving both reliability in quality assurance and scalability in handling large numbers of experiments.
Solution Approach 2:
The system dynamically adjusts analysis parameters and computational resources based on the scale of experiments being monitored. When handling billions of variants, the system automatically modifies sampling rates, aggregation levels, and computational depth to maintain detection reliability while ensuring the process remains scalable and does not become prohibitively complex or resource-intensive.
3Adaptability or versatility
If multiple experiment layers are created with users assigned to multiple buckets simultaneously, then experimentation capability is enhanced, but the risk of contamination and quality issues increases
Solution Approach 1:
The patent implements continuous feedback mechanisms that monitor experiment layers for contamination signals. The system automatically detects when users assigned to multiple buckets simultaneously are causing quality degradation, and triggers corrective actions such as isolating contaminated buckets or adjusting user assignments. This feedback loop maintains reliability by continuously detecting and correcting quality issues that arise from enhanced experimentation capabilities.
Solution Approach 2:
The system takes preliminary anti-actions by proactively identifying and isolating potential contamination sources before they can significantly degrade experiment quality. When the automated detection system identifies patterns suggesting contamination in multi-layer experiments, it preemptively triggers remediation protocols such as separating affected user groups or pausing problematic experiment layers, preventing quality degradation before it occurs.
Data Source
AI summary
The present teaching relates to managing online experiments. In one example, a plurality of experiment layers is created with respect to a plurality of online users. Each experiment layer includes at least one experiment each of which includes one or more buckets associated with respective features to be experimented on. Each of the plurality of online users is assigned to a corresponding bucket in each experiment layer, such that the user is simultaneously associated with multiple experiments in different layers. User event data related to the plurality of experiment layers are collected from the plurality of online users. One or more contaminated buckets are automatically detected based on the user event data.