Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines

• URL: http://arxiv.org/abs/2607.21173v1
• Date: Thu, 23 Jul 2026 10:59:16 GMT
• Title: Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines
• Abstract summary: We present the Artificial Intelligence (AI)-based Epidemiology Research Assistant (ARA) framework.<n>ARA integrates protocol construction, synthetic data generation, and adversarial validation into a unified pipeline.<n>We evaluate ARA on the Automated Causal Reasoning Benchmark, assessing recovery of identification strategies, causal quantities, treatment and outcome variables, and consistency between generated code and approved protocol.
• Score: 5.193393225545891
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 first constructing a protocol and then generating synthetic datasets using Structural Causal Models (SCMs) with known ground-truth effects. This synthetic-data step can also support pipeline development when access to confidential data, such as medical data, is restricted. The generated analysis is then evaluated under controlled violations of identification assumptions. We evaluate ARA on the Automated Causal Reasoning Benchmark, assessing recovery of identification strategies, causal quantities, treatment and outcome variables, and consistency between generated code and approved protocol. Protocol construction and adversarial validation did not consistently improve numerical agreement with benchmark estimates compared with standard LLM-based generation. However, they changed the failure mode: instead of silently returning causal estimates, ARA often surfaced protocol concerns, diagnostic failures, incomplete inference, or downgraded non-causal interpretations. These findings suggest that validity-first automated science systems should be evaluated not only by answer accuracy, but also by whether they indicate when causal claims are unwarranted.

Related papers

• CausalArena: Benchmarking Causal Discovery in the Foundation Model Era [47.81267771630766]
  CausalArena is a unified benchmark for causal discovery under a common protocol.<n>We show that strong performance in one benchmark regime does not reliably transfer to others.
  arXiv  Detail & Related papers  (2026-09-10T17:53:15Z)
• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• CasualSynth: Generating Structurally Sound Synthetic Data [44.80087038178069]
  Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the target domain.<n>We introduce Causal Synth, a framework that decouples causal structure generation from semantic realization, yielding synthetic data that is both causally valid and linguistically rich.
  arXiv  Detail & Related papers  (2026-05-17T16:21:01Z)
• Federated Semantic Knowledge Graphs for Laboratory Workflows: A Structured Expert Elicitation Methodology Demonstrated Through Bioanalytical Workflow Twins [0.0]
  Laboratory in pharmaceutical and biomedical research encode substantial tacit knowledge.<n>We present a structured expert elicitation methodology and federated Semantic Knowledge Graph (SKG) architecture for capturing and querying this knowledge.<n>This architecture provides the semantic world model that AI laboratory agents lack a queryable representation of where human judgment is irreplaceable, and which execution assets mask rather than detect failure.
  arXiv  Detail & Related papers  (2026-05-15T18:44:52Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• Ontology-Aware Design Patterns for Clinical AI Systems: Translating Reification Theory into Software Architecture [0.0]
  Clinical AI systems routinely train on health data structurally distorted by documentation, billing incentives, and terminology fragmentation.<n>This paper proposes seven-aware design patterns in Gang-of-Four pattern language for building clinical AI pipelines resilient to ontological distortion.
  arXiv  Detail & Related papers  (2026-04-02T06:05:11Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Procrustean Bed for AI-Driven Retrosynthesis: A Unified Framework for Reproducible Evaluation [0.0]
  RetroCast is a unified evaluation suite that standardizes heterogeneous model outputs into a common schema.<n>We evaluate leading search-based and sequence-based algorithms on a new suite of standardized benchmarks.
  arXiv  Detail & Related papers  (2025-12-08T01:26:39Z)
• A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code [49.09545217453401]
  Propensity Smelly Score (PSC) is a metric that estimates the likelihood of generating particular smell types.<n>We identify how generation strategy, model size, model architecture and prompt formulation shape the structural properties of generated code.<n> PSC helps developers interpret model behavior and assess code quality, providing evidence that smell propensity signals can support human judgement.
  arXiv  Detail & Related papers  (2025-11-19T19:18:28Z)
• CALM: A Causal Analysis Language Model for Tabular Data in Complex Systems with Local Scores, Conditional Independence Tests, and Relation Attributes [15.298086464296235]
  Causal discovery from observational data is fundamental to scientific fields like biology.<n>Existing methods, including constraint-based and score-based approaches, face significant limitations.<n>We introduce CALM, a novel causal analysis language model specifically designed for tabular data.
  arXiv  Detail & Related papers  (2025-10-10T20:19:20Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.