Local instrumental variables and latent variable models for identifying and bounding treatment effects.

Area of Science:

• Econometrics
• Statistical Modeling
• Causal Inference

Background:

• Treatment effect estimation is crucial in many fields.
• Latent variable models are used when unobserved factors influence outcomes.
• Identifying treatment effects can be challenging due to unobserved heterogeneity.

Purpose of the Study:

• To examine the relationship between treatment parameters in latent variable models.
• To develop methods for identifying treatment parameters when effects depend on observed and unobserved characteristics.
• To establish bounds for treatment parameters when identification is not possible.

Main Methods:

• Utilizing a latent variable modeling framework.
• Analyzing the dependence of treatment effects on recipient characteristics (observed and unobserved).
• Developing identification and bounding strategies for treatment parameters.

Main Results:

• Demonstrated how the relationship between parameters aids in identification.
• Provided a method to bound parameters when full identification is not achieved.
• The approach is applicable when treatment effects are heterogeneous.

Conclusions:

• The proposed methods enhance the analysis of treatment effects in latent variable models.
• Offers a flexible framework for parameter estimation under varying identification conditions.
• Contributes to robust causal inference with unobserved confounders.