A/B Test Analysis for Direct and Indirect User Effects
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Solution Overview
Problem
Existing A/B testing systems fail to accurately distinguish between direct and indirect effects on user outcomes when comparing different versions of an online platform element, particularly due to the inability to measure interactions with causally-dependent mediator elements, leading to incomplete decision-making insights.
Innovation Solution
A method and system that conduct A/B testing by splitting users into control and test groups, measuring interactions with both target and mediator elements, and using linear equations or iterative general method of moments to determine direct and indirect effect values, accounting for unmeasured causally-dependent mediator elements to provide more accurate effect analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If A/B testing measures only overall user outcomes without distinguishing direct and indirect effects, then the analysis is simpler and faster, but the decision-making insights are incomplete and inaccurate
Solution Approach 1:
The patent segments the overall effect into two distinct components: direct effect (effect independent of mediator changes) and indirect effect (effect through mediator changes). This segmentation allows precise measurement of each component separately through system of equations, resolving the contradiction by making the complex analysis structured and manageable while achieving complete decision-making insights
Solution Approach 2:
The patent introduces mediator elements as intermediaries to decompose the causal pathway. By measuring and analyzing mediator interactions, the system can separate direct effects from indirect effects, improving measurement precision while maintaining analytical tractability through the mediator framework
2Measurement precision
If A/B testing accounts for unmeasured causally-dependent mediator elements, then the effect analysis is more accurate, but the measurement and analysis become more difficult
Solution Approach 1:
The patent uses mediator elements as observable intermediaries that capture the influence of unmeasured causally-dependent factors. By modeling the mediator's relationship with both the treatment and outcome, the system can account for unmeasured confounders without directly measuring them, improving accuracy while avoiding the impossibility of measuring all causal factors
Solution Approach 2:
The patent replaces direct measurement of complex causal relationships with a mathematical modeling approach using systems of equations. This substitution allows the analysis to account for unmeasured mediators through statistical relationships rather than requiring direct observation of all causal pathways
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing an A/B test on a target element of an online platform. In one aspect, a method comprises: conducting an A/B test on a target element of an online platform, comprising: for each user in a population of users, measuring: (i) an outcome of the user interacting with the online platform, and (ii) an interaction of the user with a mediator element of the online platform; and determining, based on the A/B test, a direct effect value that estimates an expected change in user outcomes when the test version of the target element is presented instead of the control version of the target element that is caused independently of induced changes in user interaction with the mediator element.