In addition to the difficulties in defining causal models and necessary identifying assumptions, complexities and subjectivity in sensitivity analysis and model validation have greatly reduced the practicality and accessibility of causal inference approaches. In this study, we explore possibilities of accelerating model validation using a prediction framework. We will use simple examples in identifying local average treatment effect (principal causal effects) in the presence of treatment or program noncompliance. Specifically, we will focus on the use of posterior latent compliance class probability, an individual level estimate that reflects the causal model under consideration. The model validity will be assessed by how well the posterior class probability is predicted by baseline variables that are supposed to be its predictors (antecedent validators), how strongly it is associated with other intermediate outcomes (concurrent validators) and how well it predicts future outcomes it is expected to predict (predictive validators). In each validation context, we will monitor both prediction/classification accuracy and its variability.