Offline Causal Model Evaluation Using Randomized Decision Utility
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Solution Overview
Problem
Conventional personalized decision systems rely on correlation-based predictive models that fail to account for unobserved confounding factors, leading to incorrect estimation of causal effects, and existing methods for causal models either use synthetic data or predictive performance, which do not consider counterfactual outcomes or unknown confounders.
Innovation Solution
A method and system for evaluating and learning causal models using offline randomized trial data, generating optimal group labels to inform decisions, which does not rely on conditional independence assumptions or causal graph assumptions, and includes processes for obtaining and configuring datasets, calculating potential outcomes, and learning a causal model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correlation-based predictive models are used for personalized decision-making, then the system can identify patterns and make predictions, but it cannot determine causal effects due to unobserved confounding factors
Solution Approach 1:
The patent introduces an intermediary variable G (optimal group label) that mediates between the decision variable A and outcome variable Y. This mediator captures the causal structure by grouping units with similar counterfactual outcomes, enabling accurate causal effect estimation while accounting for unobserved confounders through the randomized trial framework
2Ease of manufacture
If synthetic data is used for causal model evaluation, then model learning can be performed, but the data fails to reflect real-world causal relations and eliminates the need for further learning
Solution Approach 1:
The patent extracts and utilizes the structure of randomized trial data to create a specialized evaluation framework that preserves real-world causal relations. By taking out the essential properties of randomized experiments (random assignment, counterfactual outcomes) and applying them to model evaluation, the method achieves both feasibility and accuracy without relying on synthetic data
3Productivity
If predictive performance metrics are used for causal model evaluation, then model performance can be measured, but counterfactual outcomes and unknown confounders are not accounted for
Solution Approach 1:
The patent performs preliminary action by pre-computing potential outcomes Y0(a) for all possible decision values a during the evaluation phase. This allows the method to efficiently measure causal effects by comparing pre-computed counterfactual outcomes rather than requiring new experiments, thus maintaining efficiency while achieving precise causal measurement
4Quantity of substance
If observational data is used for causal inference, then real-world data can be utilized, but confounding factors lead to incorrect causal effect estimation
Solution Approach 1:
The patent introduces an intermediary variable G (optimal group label) that mediates between the decision variable A and outcome variable Y. This mediator captures the causal structure by grouping units with similar counterfactual outcomes, enabling accurate causal effect estimation while accounting for unobserved confounders through the randomized trial framework
Data Source
AI summary
The present disclosure provides an offline method for causal model evaluation and adaptive learning engine for personalized decision-making, along with exemplary implementations thereof, comprising a specialized causal model evaluation method and a causal model learning method. The evaluation method disclosed herein utilizes randomized experimental data to estimate the decision utility of a causal model by computing its pseudo-decision utility via a quasi-randomized procedure. The learning method disclosed herein optimizes the decision outputs of a causal model by learning optimal decision group labels. The evaluation method exhibits weak dependence on assumptions regarding the data and the causal model under evaluation, does not rely on individual-level covariates, has wide applicability, avoids the problem of unobserved confounding, and produces evaluation results that are directly tied to the decision utility. The learning method yields models that achieve high personalized decision utility, is user-friendly, and is suitable for data-driven personalized decision systems.