Treatment Effect Estimation Using Nonlinear Two-Stage Instrumental Variable Estimators: Another Cautionary Note.

Area of Science:

• Econometrics
• Biostatistics
• Causal Inference

Background:

• Instrumental variable (IV) methods are crucial for estimating causal effects from observational data.
• Nonlinear two-stage residual inclusion (2SRI) is increasingly used for complex models, particularly with binary outcomes.
• Concerns exist regarding the generalizability and potential biases of these advanced methods.

Purpose of the Study:

• To evaluate simulation evidence for nonlinear 2SRI IV methods in estimating average treatment effects (ATE) from observational data.
• To investigate the potential bias of 2SRI under scenarios of essential heterogeneity and unique marginal patient characteristics.

Main Methods:

• Assessed potential bias of linear and nonlinear IV methods for ATE and local average treatment effects (LATE).
• Utilized simulation models with binary outcomes and binary endogenous treatments.
• Varied settings based on the relationship between treatment effectiveness and treatment choice.

Main Results:

• Nonlinear 2SRI models yielded substantially biased ATE and LATE estimates when marginal patient treatment-outcome relationships differed from the general population.
• Linear IV estimates for LATE exhibited low bias across all simulated scenarios.

Conclusions:

• While nonlinear 2SRI is favored for its perceived unbiasedness in complex models, its validity hinges on assumptions about treatment effect heterogeneity and choice.
• Researchers must carefully consider the conditions under which nonlinear 2SRI is applied to avoid biased causal effect estimates.