Confounding-Aware Feature Impact Measurement Using Double Machine Learning
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Solution Overview
Problem
Current methods for determining the impact of product features on customer satisfaction, such as NPS, are resource-intensive, time-consuming, and face challenges with confounding variables, making it difficult to efficiently and accurately assess feature effects.
Innovation Solution
A data processing system utilizing double machine learning (DML) to analyze feature usage effects on NPS by aggregating telemetry and NPS data, applying confounding variables, and generating visual representations to efficiently determine feature impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to examine NPS to determine effects of in-product functionalities, then measurement precision is improved, but productivity deteriorates due to extensive computing resources and time requirements
Solution Approach 1:
The patent segments the analysis process into distinct stages: data collection, confounder identification, double machine learning modeling, and result interpretation. This segmentation allows parallel processing of different data streams and computational tasks, improving overall processing speed while maintaining measurement accuracy through specialized handling at each stage.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying and storing confounding variables before the main analysis, pre-processing telemetry data to match NPS survey timing, and pre-configuring the double machine learning framework. These preliminary steps reduce computational burden during actual analysis, enabling faster processing without sacrificing measurement precision.
2Measurement precision
If current methods are used to examine NPS to determine effects of in-product functionalities, then measurement precision is improved, but loss of time increases due to significant processing time requirements
Solution Approach 1:
The system performs preliminary actions by pre-identifying confounding variables, pre-matching telemetry data with NPS survey timestamps, and pre-configuring the double machine learning framework. These preliminary steps are cached and reused across multiple analyses, dramatically reducing the time required for actual feature impact assessment while preserving measurement accuracy.
Solution Approach 2:
The patent creates simplified copies of the complex analysis pipeline for common scenarios. Once the double machine learning model is trained on comprehensive data, it generates predictive models that can quickly estimate feature impacts on NPS without re-running the full computational process, reducing analysis time while maintaining precision through the trained model's accuracy.
3Productivity
If double machine learning is implemented to determine treatment effects, then productivity is improved through reduced processing time, but device complexity increases due to sophisticated modeling requirements
Solution Approach 1:
The patent introduces an intermediary layer between raw data and final results: the double machine learning framework acts as a mediator that automatically handles confounder adjustment and causal effect estimation. This intermediary absorbs the complexity of sophisticated modeling, presenting simplified results to users while maintaining high processing efficiency through automated computations.
Solution Approach 2:
The double machine learning implementation performs self-service by automatically identifying confounding variables from telemetry data, automatically matching them with NPS surveys, and automatically executing the causal inference algorithms. This self-service capability reduces the need for manual configuration and intervention, managing system complexity through automation while improving productivity.
4Measurement precision
If comprehensive data collection is performed to account for confounding variables, then measurement precision is improved, but use of energy increases due to extensive data processing
Solution Approach 1:
The patent extracts only the essential confounding variables needed for causal inference from the comprehensive telemetry data, rather than processing all available data. The double machine learning framework identifies and extracts relevant confounders based on their relationship with both the treatment and outcome, reducing computational energy consumption while maintaining measurement precision through focused analysis of critical variables.
Data Source
AI summary
A method and system for determining the causal effect of a treatment on a product includes extracting telemetry data from a use of the product, and net promoter score values associated with the product in a plurality of batches. The telemetry data and net promoter score values are aggregated into an aggregated data structure to generate a plurality of aggregated data structures, where each aggregated data structure of the plurality of data structures corresponds to one batch. The plurality of data structures are then appended to generate an aggregated dataset and data preprocessing is performed on the aggregated dataset to generate a filtered dataset. A request is then transmitted to a double machine learning (DML) cluster to generate treatment effect scores for the filtered dataset, the DML cluster including a treatment model and an effect model, wherein each of the models receives the confounding variables to be used in debiasing. The treatment effect scores are received as an output from the DML cluster and a visual representation of the treatment effect scores is generated via a data visualization engine.