Accounting for Latent Covariates in Average Effects from Count Regressions

Area of Science:

• Statistics
• Biostatistics
• Econometrics

Background:

• Treatment effectiveness is often assessed using negative binomial regression for count outcomes.
• Estimating treatment effects typically requires all covariates to be observed (manifest) variables.
• Latent variables, measured by fallible indicators, introduce measurement error that can bias treatment effect estimates.

Purpose of the Study:

• To propose a novel approach for estimating average and conditional treatment effects.
• To address challenges posed by latent covariates in regression models with a logarithmic link function.
• To extend existing moment-based methods by incorporating latent variables and multiple covariates.

Main Methods:

• Utilizing a multigroup Structural Equation Modeling (SEM) framework for count data.
• Extending a previously developed moment-based approach.
• Integrating latent and manifest covariates within the regression model.
• Employing a logarithmic link function for count outcomes.

Main Results:

• The proposed method allows for the estimation of treatment effects in the presence of latent covariates.
• It effectively controls for measurement error, mitigating attenuation bias.
• The approach is adaptable to models with multiple latent and manifest covariates.

Conclusions:

• The new approach provides unbiased treatment effect estimates even with latent covariates.
• It offers a flexible framework within SEM for analyzing count data with complex covariate structures.
• The method enhances the accuracy of treatment effect estimation in statistical modeling.