Predictive AI systems are increasingly embedded in tasks once reserved for expert judgment, raising a question for information systems research: Does algorithmic assistance sharpen professional judgment, or erode it? Before committing to a longitudinal registry study, this paper asks a logically prior question: does the identification strategy such a study would use actually work, and under what conditions would it fail? We construct a synthetic panel of 50 experts evaluating 40 cases across four periods (2,000 observations per replication), embedding a known relationship among algorithmic reliance, decision variation, and diagnostic error. Across 500 Monte Carlo replications, a two-way fixed-effects (TWFE) framework recovers the built-in decision-homogenization effect and the U-shaped reliance-error relationship with low bias and near-nominal coverage. We then extend this baseline in two ways. First, comparing TWFE against pooled OLS under independent and confounded data-generating processes shows that pooled OLS is essentially unbiased in the first case and biased by roughly 52% in the second, while TWFE remains unbiased in both cases, clarifying precisely when the fixed-effects correction does identifying work instead of improving precision. Second, stress-testing the estimating equation against four departures from its baseline assumptions, a kinked threshold relationship, heterogeneous reliance slopes, a logistic error process, and reliance that responds dynamically to past errors, shows the pipeline remains well-calibratedunder slope heterogeneity, but that coverage and the power of the Lind-Mehlum U-shape test both deteriorate markedly under a logistic error process and dynamic feedback. All results describe estimator behavior under a known synthetic data-generating process; they are not empirical claims about real professionals.