Instrumental variable estimation of truncated local average treatment effects

Area of Science:

• Statistics
• Econometrics
• Causal Inference

Background:

• Instrumental variable (IV) analysis is crucial for addressing unmeasured confounding in nonrandomized studies.
• The local average treatment effect (LATE) is a key causal estimand identified by IVs, relying on weaker assumptions than other IV methods.
• Existing weighting estimators for LATE can suffer from high variance with weak IVs and small complier populations.

Purpose of the Study:

• To propose a novel truncated local average treatment effect (LATE) estimation method.
• To enhance the reliability and precision of causal effect estimation in the presence of weak instrumental variables.
• To provide a more robust alternative to standard LATE estimation when the target complier population is small.

Main Methods:

• Introduced a truncated LATE approach that identifies and removes subjects contributing substantially to a weak IV based on their complier probabilities.
• Developed an inference method for the proposed truncated LATE estimand.
• Utilized simulation studies and real-world data experiments to evaluate the method's performance.

Main Results:

• The proposed truncated LATE method demonstrated improved estimation precision compared to the standard LATE.
• The method effectively addresses the challenges posed by weak instrumental variables in causal effect estimation.
• Simulation and real data results confirmed the practical utility and enhanced reliability of the truncated LATE.

Conclusions:

• The truncated LATE offers a more precise and reliable estimation strategy for causal effects when dealing with weak instrumental variables.
• This approach provides a valuable tool for researchers seeking to mitigate confounding in observational studies with limited instrument strength.
• Further discussion on the interpretation and inference for the truncated LATE estimand is provided.