A Diagnostic Approach to Causal Inference

Music City Center 

This work has been motivated by the lack of accessible methods even for relatively simple and common causal inference problems in practice (e.g., treatment noncompliance). This presentation revisits the use of Gaussian mixtures as a potentially accessible method of model identification in the context of principal stratification. Relying on such parametric conditions for causal identification has been mostly considered as risky without clear paths to conducting sensitivity analysis. Turning this situation around, the proposed diagnostic approach provides the means for assessing the quality of causal effect estimates from parametric identification. Our strategy for constructing diagnostic measures is unique - we observe how parametric estimation responds to varying degrees of nonparametrically identifying restrictions. In other words, parametric and nonparametric identification methods are jointly used to generate diagnostic indices, which will tell us how good or biased the parametrically identified causal effect estimates are. The presentation will highlight potential benefits of this under-explored causal approach such as easiness in implementation, quality control and automation.

Principal causal effects

Gaussian mixtures

Parametric identification

Nonparametric identification

Diagnostic indices

Automation