Joint Causal Effect Estimation With Adaptive LASSO Weighting
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Solution Overview
Problem
Existing methods struggle to estimate joint causal effects of multiple exposures on health outcomes in the presence of high-dimensional covariates, leading to biased estimates due to confounding variables and the inability to select relevant variables effectively.
Innovation Solution
An estimation method combining nonparametric multiple treatments covariate balancing generalized propensity score (npmtCBGPS) with adaptive LASSO (mtGOAL) to select covariates, calculate balance weights, and use inverse probability weighting for unbiased estimation of joint causal effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If balance-based GPS methods (npCBGPS) are used to estimate causal effects, then robustness to model misspecification is improved, but the ability to handle multiple exposures and high-dimensional covariates deteriorates
Solution Approach 1:
The patent merges the balance-based GPS method (npCBGPS) with the adaptive LASSO variable selection technique to create a unified framework that simultaneously achieves robust causal effect estimation and effective variable selection for multiple exposures with high-dimensional covariates
Solution Approach 2:
The patent extends the npCBGPS method from single-exposure to multi-exposure settings, making it universally applicable to complex real-world scenarios where multiple environmental factors jointly influence health outcomes while maintaining robustness properties
2Reliability
If all pre-exposure covariates are included in the GPS model, then confounding control is improved, but model complexity and computational burden increase
Solution Approach 1:
The patent extracts and selects only the most relevant confounding variables and prognostic covariates from the high-dimensional pre-exposure covariate set using adaptive LASSO, removing unnecessary variables that increase model complexity without contributing to confounding control
Solution Approach 2:
The patent performs preliminary variable selection using adaptive LASSO before constructing the GPS model, identifying and retaining only the important confounders and prognostic covariates in advance, thereby simplifying the subsequent causal effect estimation process
Data Source
AI summary
Disclosed is an estimation method for joint causal effects of multiple exposures based on high-dimensional independent variables, including the following steps: reducing a dimension by using a modified adaptive least absolute shrinkage and selection operator (LASSO); calculating balance weights by using a nonparametric multiple treatments covariate balancing generalized propensity score (npmtCBGPS) method, and determining an optimal value of a tuning parameter by taking a minimum multiple treatment dual-weighted coefficient (mtDWC) as a criterion; and estimating joint causal effects of multiple continuous exposure factors on an outcome variable by using an inverse probability weighting (IPW) method. According to the present invention, in a framework of a GOAL method, a multiple treatments GOAL (mtGOAL) method by combining the npmtCBGPS method with the adaptive LASSO, and a method capable of estimating joint causal effects of multiple continuous exposure factors on an outcome variable in the presence of high-dimensional covariates are proposed.