Automated Online Experiment Stopping Rule Selection
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Solution Overview
Problem
Determining an appropriate stopping point for online experiments is challenging due to the need for understanding the data's characteristics and statistical properties, which are often unavailable before data collection begins, leading to potential inefficiencies in resource usage and accuracy of inferences.
Innovation Solution
The implementation of a system that automates the selection of stopping rules for online experiments by dynamically analyzing data in real-time, using statistical models to determine appropriate stopping conditions based on the data's characteristics, and applying these rules to ensure convergence criteria are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection continues longer to improve inference accuracy, then measurement precision improves, but loss of time and resource expenditure increases
Solution Approach 1:
The system continuously monitors data characteristics and statistical properties during the experiment, using this feedback to dynamically adjust the stopping decision. The automated stopping rule evaluates incoming data in real-time and provides feedback on whether the convergence criterion is met, allowing the experiment to stop at the optimal point without manual intervention.
Solution Approach 2:
The system performs self-evaluation by automatically assessing its own data characteristics and determining when sufficient data has been collected. The automated stopping rule independently evaluates the convergence criterion and makes the stopping decision without external input, enabling the system to serve itself in determining the experiment's endpoint.
2Reliability
If stopping rule selection is done manually with statistical expertise, then reliability of stopping decision improves, but device complexity and ease of operation worsen
Solution Approach 1:
The system embeds statistical expertise directly into the automated stopping rule, which independently evaluates data characteristics and makes stopping decisions without requiring external statistical analysts. The system serves itself by incorporating the necessary statistical knowledge into the automation logic.
Solution Approach 2:
The automated stopping rule is designed to handle multiple data types and experimental configurations universally. It can evaluate various data characteristics (mean, variance, convergence rates) and apply appropriate stopping criteria across different experiment types, making the system versatile without requiring separate specialized rules for each case.
3Measurement precision
If data collection is extended to ensure sufficient sample size, then measurement precision improves, but loss of time increases
Solution Approach 1:
The stopping rule dynamically adjusts the experiment duration based on the actual data characteristics observed during collection. Rather than using a fixed predetermined sample size, the system continuously evaluates whether the current data provides sufficient confidence for inference, allowing the experiment to adapt its duration to the actual information gain rate.
Solution Approach 2:
The system uses real-time feedback from data evaluation to determine when the confidence level has reached an acceptable threshold. The automated stopping rule monitors the convergence of statistical estimates and provides feedback on whether additional data collection would meaningfully improve inference confidence, enabling early stopping when sufficient confidence is achieved.
Data Source
AI summary
In certain embodiments, a method includes obtaining, by a processing device, a pre-determined number of data points from an online experiment; processing, by the processing device, the data points to determine whether the data points exhibit at least one characteristic; selecting, by the processing device and in response to the data points exhibiting the at least one characteristic, a first stopping rule of a plurality of stopping rules, wherein the first stopping rule corresponds to the at least one characteristic; applying, by the processing device, the first stopping rule to determine whether a first convergence criterion is met; and stopping, when the first convergence criterion is met, the online experiment.