Forecast-Based Experiment Analysis for A/B Test Variance Reduction
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Solution Overview
Problem
Experimental analysis in software development, such as A/B testing, suffers from high variance leading to low statistical significance and prolonged running times, causing infrequent software updates.
Innovation Solution
Implement a method using forecasts as covariates to reduce variance by employing time series modeling and generating auxiliary metrics through an automation pipeline, which accounts for temporal behavior and reduces variance without requiring extensive feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental analysis is performed to determine software updates, then statistical significance can be achieved, but the running time becomes excessively long
Solution Approach 1:
The patent applies preliminary action by using historical data and time series models to forecast future performance metrics before the actual experiment runs. By pre-computing forecasts based on past patterns, the system establishes expected outcomes in advance, allowing the experiment to focus only on detecting deviations from these pre-established expectations rather than building statistical significance from scratch during the experiment.
Solution Approach 2:
The patent introduces forecasts as an intermediary element between historical data and experimental results. The forecasts act as a mediator that translates historical patterns into predicted future values, which then serve as benchmarks for evaluating experimental outcomes. This intermediary approach allows the system to leverage historical insights without requiring prolonged experimental running to achieve statistical significance.
2Reliability
If experimental analysis is performed to update software, then decisions can be made with statistical confidence, but software updates become infrequent
Solution Approach 1:
By pre-computing forecasts based on historical data patterns, the system establishes reliable decision-making frameworks in advance. These forecasts provide immediate benchmarks that can be used to evaluate software updates quickly, enabling more frequent updates with maintained decision confidence without requiring prolonged experimental analysis for each update.
Solution Approach 2:
The system implements feedback by continuously comparing actual experimental results against the forecasts generated from historical data. This feedback mechanism allows rapid assessment of whether updates are warranted, as deviations from forecasted values can trigger immediate decisions. The feedback loop enables frequent, data-driven updates while maintaining statistical confidence through continuous validation against historical patterns.
3Measurement precision
If traditional experimental analysis is used, then variance can be managed through sample size, but the complexity and time required increase
Solution Approach 1:
The patent changes the approach from controlling variance through sample size to controlling variance through forecast-based expectations. By transforming the problem into a forecast deviation analysis, the system manages variance not by increasing samples but by using time series models to predict and account for temporal patterns, thereby reducing the complexity of experimental design while maintaining precision.
Data Source
AI summary
Various embodiments can reduce variance in a target metric (e.g., experiment outcome). Embodiments can use historical pre-experiment outcomes to predict a metric (forecasts) that is expected for a future measurement time. The forecasts can then be used as a covariate to reduce the variance of the target metric. When predicting forecasts, various embodiments can use an automation pipeline that can generate better and quicker forecasts. When there are multiple covariates that may be considered to reduce variance in the target metric, various embodiments can use a closed from solution for determining optimal coefficient of each covariate.