Causal Discovery Benchmark: SciDEX vs LLM Baselines

Theorist position for analysis SDA-causal-benchmark-20260428-035713: Causal Discovery Benchmark: SciDEX vs LLM Baselines

Context: Recorded benchmark methods: A_scidex_debate_engine, B_gpt4_zeroshot, C_gpt4_causal_reasoning, D_chance_baseline.

Primary claim: whether debate-structured causal reasoning improves calibration over direct LLM baselines is a debate-worthy mechanism or quality claim, not just a restatement of the analysis title. The strongest version predicts a proximal readout that changes before a late outcome. For this causal discovery benchmark, the debate should preserve the named strata and entities: SciDEX, causal discovery, calibration, benchmark.

The constructive hypothesis is that the analysis can advance SciDEX's world model if it binds the question to a falsifier. The priority test is expand the gold-standard causal set, report accuracy/ECE/Brier with confidence intervals, and ablate debate roles against identical evidence packets. A positive result would require concordant movement of the proximal readout and a disease-relevant or reproducibility-relevant endpoint; a negative result would downgrade the claim rather than merely mark the analysis as inconclusive.

For the downstream Atlas and Exchange layers, the useful artifact is a debated hypothesis with explicit evidence requirements, not a generic confidence score. The claim should therefore carry a clear action: validate the mechanism, strengthen the benchmark, or revise the preregistered target based on the specified falsifier.

Skeptic critique for analysis SDA-causal-benchmark-20260428-035713: Causal Discovery Benchmark: SciDEX vs LLM Baselines

The analysis question is substantive, but the current record does not by itself prove the claim. The main dissent is: a small or weakly curated benchmark can make calibration differences look meaningful even when the model is exploiting prompt artifacts rather than causal structure.

The debate should reject overclaiming in three forms. First, association or benchmark performance should not be treated as causality without a design that separates cause from consequence. Second, a positive average effect can hide subgroup failure across SciDEX, causal discovery, calibration, benchmark. Third, an analysis that lacks provenance, environment capture, or preregistered endpoints can produce plausible but non-reproducible conclusions.

A decisive falsifier would be failure of expand the gold-standard causal set, report accuracy/ECE/Brier with confidence intervals, and ablate debate roles against identical evidence packets to move the predicted proximal endpoint under adequate power and controls. The strongest alternative explanation is that the observed signal is a disease-stage marker, prompt or notebook artifact, or compensatory response rather than an upstream driver.

Domain expert assessment for analysis SDA-causal-benchmark-20260428-035713: Causal Discovery Benchmark: SciDEX vs LLM Baselines

The practical path is staged. Stage 1 should lock the data inputs, covariates, and endpoints. Stage 2 should run the most direct validation: expand the gold-standard causal set, report accuracy/ECE/Brier with confidence intervals, and ablate debate roles against identical evidence packets. Stage 3 should connect the result to a reusable SciDEX artifact: a promoted hypothesis, a benchmark row with confidence intervals, a notebook reproducibility badge, or a revised preregistration.

Feasibility is moderate because the question is specific enough to test, but the intervention point may be less direct than the named entity. For therapeutic claims, safety and timing matter; for benchmark and methodology claims, calibration, reproducibility, and leakage controls matter. The near-term deliverable should be a falsifiable validation plan rather than a premature declaration of success.

Consensus is strongest around using this analysis to sharpen the world model. Dissent remains around causal direction, artifact robustness, and translational tractability.