An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with...

Area of Science:

• Machine Learning
• Causal Inference
• Deep Learning

Background:

• Estimating causal effects is challenging due to high-dimensional confounding variables.
• Existing methods struggle with nonlinear dependencies and complex covariate structures.

Purpose of the Study:

• To develop a novel deep learning framework, CausalEGM, for nonlinear dimension reduction and generative modeling.
• To enhance the accuracy of causal effect estimation in both binary and continuous treatment scenarios.
• To mitigate confounding effects by extracting relevant latent features.

Main Methods:

• CausalEGM employs a bidirectional transformation between high-dimensional covariates and a low-dimensional latent space.
• It models dependencies of latent variables on treatment and response to identify key confounding factors.
• The framework integrates dimension reduction with generative modeling for causal analysis.

Main Results:

• CausalEGM demonstrates superior performance compared to existing methods in both binary and continuous treatment settings.
• Performance improvements are particularly significant with large sample sizes and high-dimensional covariates.
• The method effectively extracts latent features that confound the treatment-response relationship.

Conclusions:

• CausalEGM offers a powerful approach for causal inference by addressing high-dimensional confounding through deep learning.
• The framework provides theoretical guarantees, including excess risk bounds and consistency.
• CausalEGM advances dimension reduction techniques within the field of causal inference.