Causal Inference Framework for Paired Event Data
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Solution Overview
Problem
Current data collection technologies face limitations in capturing repeated occurrences and provide limited coverage for event datasets, assuming discrete time in data model generation, which leads to inaccuracies in data prediction.
Innovation Solution
A computer-implemented method that identifies data variables in a multivariate event dataset, formalizes causal inference between them, generates a structural framework for average effect values, and calculates inverse propensity scores to adjust for covariates, improving data collection efficiency and accuracy by quantifying causal effects in dynamic, asynchronous event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If discrete time assumption is used in data model generation, then data collection process is simplified, but measurement precision of event outcomes deteriorates
Solution Approach 1:
The patent transitions from discrete time parameter assumption to continuous time parameter modeling. By treating time as a continuous parameter rather than discrete units, the model achieves higher measurement precision for event outcomes while maintaining operational feasibility through automated computational procedures.
2Ease of manufacture
If traditional data collection methods are used, then implementation is straightforward, but coverage of repeated occurrences in event datasets is limited
Solution Approach 1:
The patent implements preliminary identification and formalization of causal relationships between variables before conducting the main analysis. By pre-establishing the structural framework of average effect values and calculating inverse propensity scores in advance, the method efficiently captures repeated occurrences without complicating the overall implementation process.
3Measurement precision
If causal inference is formalized between multiple variables, then accuracy of average treatment effect estimation is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex causal inference problem into distinct computational stages: (1) identifying data variables, (2) formalizing causal inference between variables, (3) generating structural framework of average effect values, and (4) calculating inverse propensity scores. This segmentation reduces perceived complexity by breaking down the overall system into manageable, sequential components.
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.