A Latent Variable Approach for Causal Effect Estimation Under Misclassified Treatment Assignment.

Area of Science:

• Biostatistics
• Epidemiology
• Causal Inference

Background:

• Misclassification of treatment assignment is a significant challenge in observational studies, potentially biasing causal effect estimates.
• Existing methods often require a separate validation dataset to correct for misclassification.
• Unaddressed misclassification can undermine the reliability of findings from observational research.

Purpose of the Study:

• To propose a novel latent variable approach for robust causal effect estimation in the presence of treatment misclassification.
• To develop a method that does not require a validation dataset, simplifying the analysis process.
• To enhance robustness against measurement error model misspecification using neural networks.

Main Methods:

• A potential outcome modeling framework is employed, treating true treatment assignment as a latent variable.
• A likelihood function is constructed incorporating an outcome model, a measurement error model for misclassification, and a propensity score model.
• Neural networks are integrated into the measurement error model to improve robustness against misspecification.

Main Results:

• The proposed method demonstrates good performance across various misclassification assumptions in simulations.
• The use of neural networks effectively mitigates the impact of functional form misspecification in the measurement error model.
• The method was successfully illustrated using a synthetic dataset from the Right Heart Catheterization (RHC) study.

Conclusions:

• This latent variable approach provides a flexible and robust framework for causal inference with misclassified treatment assignment.
• The method enhances the reliability of causal effect estimates when validation data is unavailable.
• It offers a valuable tool for researchers dealing with treatment misclassification in observational studies.