Prescriptive ReLU Network for Interpretable Treatment Policy Learning
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Solution Overview
Problem
Existing methods for determining the best treatment option from observational data face challenges such as limited interpretability and the need for counterfactual outcomes, which are not directly observable, and are often cumbersome to implement due to complex and opaque policies.
Innovation Solution
A prescriptive ReLU network model that uses a piecewise linear neural network to partition the input space into disjoint polyhedra and convert it into an equivalent prescriptive tree with hyperplane splits, allowing for flexible incorporation of constraints and balancing prescriptive performance with interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine treatment options from observational data, then implementation is simpler, but prescriptive accuracy and interpretability are inferior
Solution Approach 1:
The patent segments the complex prediction task into two distinct components: a ReLU neural network for accurate counterfactual prediction and a prescriptive tree for interpretable policy representation. This segmentation allows each component to specialize - the neural network handles the mathematical complexity of counterfactual estimation while the tree structure provides human-readable decision rules, thereby resolving the contradiction between accuracy and interpretability
Solution Approach 2:
The prescriptive tree acts as an intermediary between the complex neural network model and the final treatment decision. It translates the neural network's continuous predictions into discrete, actionable policy rules that are both accurate (inherited from the neural network) and interpretable (through the tree's structured decision paths), thus bridging the gap between model complexity and practical usability
2Measurement precision
If complex policies are used to achieve accurate treatment recommendations, then prescriptive accuracy improves, but interpretability deteriorates
Solution Approach 1:
By separating the predictive function (neural network) from the policy representation function (prescriptive tree), the patent maintains high accuracy through the neural network while preserving interpretability through the tree's explicit decision rules. Each segment serves its specialized function without compromising the other
Solution Approach 2:
The patent transforms the 'color' or form of the model output from continuous probabilistic predictions (hard to interpret) to discrete categorical decisions with explicit reasoning paths (easy to interpret). The prescriptive tree provides a structured, visualizable decision framework that maintains the accuracy of underlying complex models while presenting results in an interpretable format
3Ease of operation
If counterfactual outcomes are directly observed, then treatment evaluation becomes simpler, but data availability is limited since counterfactuals are not observable
Solution Approach 1:
The patent replaces the mechanical requirement of directly observing counterfactual outcomes with a computational model (ReLU neural network) that estimates counterfactual predictions from observable data. This substitution allows the system to evaluate treatment effects without needing actual counterfactual observations, overcoming the fundamental limitation of observational data while maintaining evaluation capability
Data Source
AI summary
A method, system, and computer program product are configured to: train an artificial neural network (ANN) model using a dataset comprising observational data including treatment data, outcome data, and covariate data, wherein the ANN model includes rectified linear unit (ReLU) activation functions and K number of output nodes corresponding to K number of treatment options; and create a prescriptive tree based on the ANN model, wherein each leaf node of the prescriptive tree corresponds to one of the treatment options, and wherein the prescriptive tree is configured to indicate one of the treatment options for a particular set of features of the covariate data.