Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines
While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes successfully yet relies on invalid causal assumptions. We present the Artificial Intelligence (AI)-based Epidemiology Research Assistant (ARA), a framework that makes these failures visible by explicitly encoding causal design principles, study-specific assumptions, and methodological constraints. ARA integrates protocol construction, synthetic data generation, and adversarial validation into a unified pipeline. The framework translates natural language research questions into structured causal protocols and executable analysis code by fir...
| Authors | Irena Girshovitz, Dan Zeltzer, Ran Gilad-Bachrach |
|---|---|
| Primary category | cs.LG |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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