Combined Branch Condition Analysis for Causal Tree Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating heterogeneous causal effects, such as those described in 'Recursive partitioning for heterogeneous causal effects' by Athey et al., face challenges with deepening causal trees, leading to decreased sample sizes and learning accuracy, particularly in fields like medicine where populations are often small.
Innovation Solution
An analysis device that acquires and combines branch conditions to improve estimation accuracy by dividing data based on combined branch conditions and searching for a decision tree, using a search unit to stratify patient populations and learn a loss function that weights treatment effects for improved prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the causal tree is deepened to find complicated branch conditions, then the accuracy of estimation is improved, but the number of samples decreases and learning accuracy decreases
Solution Approach 1:
The patent segments the population into multiple subpopulations based on combined branch conditions (combinations of multiple factors) rather than single factors. This segmentation allows the model to capture heterogeneous treatment effects across different subgroups while maintaining sufficient sample sizes in each segment by strategically combining multiple branching conditions at once.
Solution Approach 2:
The patent transitions from analyzing single-factor branch conditions to multi-factor combined branch conditions, effectively adding dimensions to the analysis. By considering combinations of multiple factors simultaneously, the model can identify more nuanced subpopulations without requiring excessive tree depth, thus preserving sample sizes while improving estimation accuracy.
2Measurement precision
If the causal tree is deepened to find complicated branch conditions, then the accuracy of estimation is improved, but learning accuracy decreases
Solution Approach 1:
The patent segments the learning process into multiple stages: first identifying individual factor effects, then combining them into multi-factor branch conditions. This segmented approach prevents the model from being overwhelmed by the complexity of deep trees, maintaining learning accuracy while still achieving high estimation accuracy through combined conditions.
Solution Approach 2:
The patent performs preliminary analysis of individual factor effects before combining them into complex branch conditions. This preliminary action establishes a foundation of reliable single-factor relationships, which then serve as building blocks for more complex combined conditions, ensuring that the learning process remains accurate even as the model becomes more sophisticated.
3Measurement precision
If multiple branch conditions are used to stratify patient populations, then the accuracy of treatment effect estimation is improved, but the sample size of each subpopulation decreases
Solution Approach 1:
The patent merges multiple single-factor branch conditions into combined branch conditions that evaluate multiple factors simultaneously. This merging reduces the total number of splits required in the tree structure, thereby maintaining larger sample sizes in each terminal node while still achieving fine-grained stratification of patient populations for accurate treatment effect estimation.
Data Source
AI summary
An analysis device includes an acquisition unit that acquires, for a plurality of pieces of data to be analyzed having a value of each factor in a factor group, combined branch conditions obtained by combining a plurality of branch conditions in the factor group and values of the combined branch conditions of each piece of data to be analyzed of the plurality of pieces of data to be analyzed, and a search unit that divides the plurality of pieces of data to be analyzed having the values of the combined branch conditions on the basis of the combined branch conditions and searches for a first decision tree.