Entropy Balanced Population Measurement for Multi-Channel Attribution
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Solution Overview
Problem
Existing methods for measuring content exposure across multiple channels face challenges in attributing results to specific exposure vectors while maintaining participant confidentiality, especially when exposure occurs through multiple vectors or channels.
Innovation Solution
The use of entropy balancing and linear regression analysis with a double-encrypted intersection-based extraction mechanism to preprocess and analyze panel data, allowing for covariate balance and anonymous estimation of exposure effects across multiple exposure vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If single-source data panels are used to measure content exposure, then participant confidentiality is maintained, but the ability to determine attribution across multiple exposure vectors is lost
Solution Approach 1:
The patent segments the measurement system into multiple independent data panels, each maintaining participant confidentiality separately. By collecting data from multiple panels and using statistical aggregation, the system recovers attribution information across exposure vectors without compromising individual panel confidentiality. This segmentation allows the system to preserve sensitive information in each panel while reconstructing comprehensive attribution patterns through controlled statistical analysis.
2Measurement precision
If multiple data sources are combined to improve measurement accuracy, then attribution precision improves, but participant confidentiality is compromised
Solution Approach 1:
The patent introduces statistical aggregation and entropy balancing as intermediary processes between multiple data sources and the final attribution analysis. These intermediaries allow the system to combine information from multiple panels to improve measurement precision while preventing direct access to individual participant data. The intermediary layer transforms raw panel data into aggregated statistical measures that preserve confidentiality while enabling accurate attribution across exposure vectors.
3Measurement precision
If entropy balancing is applied to achieve covariate balance, then estimation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent applies entropy balancing by transforming the original data parameters through weighted adjustments that achieve covariate balance. This parameter transformation improves estimation accuracy by correcting for selection biases and unequal representation across panels. The entropy balancing process modifies the weight parameters of panel data to satisfy balance conditions, thereby improving the accuracy of treatment effect estimates while managing processing complexity through systematic algorithmic approaches.
Data Source
AI summary
The present disclosure provides systems and methods for entropy balanced population measurement. Entropy balancing is a statistical technique for preprocessing data to achieve covariate balance. Weighting coefficients may be dynamically adjusted to satisfy balance conditions or constraints to adjust for inequalities in representation, thereby improving covariate moments. Using entropy balancing and linear regression analysis with panel content exposure and results data provides a mechanism to estimate the effects of multiple exposure vectors simultaneously, including instances where panelists are exposed to a vector multiple times. Data may be obfuscated or anonymized for preprocessing via a double-encrypted intersection-based extraction mechanism, allowing both measurement systems and panel providers to retain confidential information.