Causal Inference Device for Treatment Effect Estimation
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Solution Overview
Problem
Current methods for estimating treatment effects, such as propensity score analysis and deep learning, face challenges in accurately identifying similar patients and explaining the importance of feature vectors, leading to difficulties in determining effective treatments, especially in medical contexts where repeated experiments on the same patient are not feasible.
Innovation Solution
A causal relation inference device that calculates internal, reallocation, and pointwise weight vectors based on learning parameters to infer assignment variables with high accuracy, enabling precise estimation of treatment effects and patient stratification, thereby facilitating better treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If logistic regression is used to calculate propensity score, then the method is widely applicable and easy to implement, but prediction accuracy is low and similar patients cannot be identified
Solution Approach 1:
The patent transforms the propensity score calculation from traditional logistic regression to a deep learning neural network model. This parameter change enables the system to learn complex non-linear relationships in medical data, significantly improving prediction accuracy while maintaining computational feasibility through standardized neural network architectures and training procedures.
Solution Approach 2:
The patent replaces the mechanical statistical method (logistic regression) with an intelligent system (deep learning neural network). This substitution allows the system to automatically learn feature representations and interaction patterns from data, achieving superior prediction accuracy without requiring manual feature engineering or statistical assumptions.
2Measurement precision
If Deep Learning is used to achieve high prediction accuracy, then similar patients can be identified accurately, but importance of feature vectors cannot be explained
Solution Approach 1:
The patent segments the neural network into multiple layers with distinct functions: encoding layers for feature extraction, a bottleneck layer for dimensionality reduction, and decoding layers for reconstruction. This segmentation enables the system to identify and explain the importance of specific feature vectors at different processing stages, providing interpretability while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary explanation module that bridges the gap between the black-box neural network and human understanding. This module analyzes the internal representations and transformations within the network to generate human-interpretable explanations of feature importance, allowing physicians to understand the basis of predictions without sacrificing accuracy.
3Measurement precision
If Deep Learning neural network is used for propensity score calculation, then high prediction accuracy is achieved, but the black-box nature makes it difficult for physicians to trust the determination criteria
Solution Approach 1:
The patent implements feedback mechanisms where the neural network's internal states and feature transformations are continuously monitored and fed back to an explanation module. This feedback loop generates real-time interpretability information that can be presented to physicians, building trust in the system's decision-making process while maintaining high prediction accuracy.
Solution Approach 2:
The patent performs preliminary analysis of feature importance and patient similarity criteria before making final predictions. By pre-computing and presenting the rationale for predicted values, the system allows physicians to review and understand the determination criteria beforehand, enhancing trust and acceptability of the high-accuracy predictions.
Data Source
AI summary
To infer a value of an assignment variable with high accuracy. In a causal relation inference device including a processor configured to execute a program and a storage device storing the program, the processor is configured to execute a first calculation process of calculating an internal vector based on a feature vector of a plurality of samples and a first learning parameter, a second calculation process of calculating a reallocation vector based on a second learning parameter and the internal vector calculated by the first calculation process, and a third calculation process of calculating a pointwise weight vector for each of the plurality of samples based on a third learning parameter and the reallocation vector calculated by the second calculation process.