From Analysis:
Causal Discovery Benchmark: SciDEX vs LLM Baselines
How does SciDEX's debate-engine compare to other LLM methods for causal discovery?
Claims from this analysis should be evaluated across SciDEX, causal discovery, calibration, benchmark; pooled effects are insufficient when causal direction, cell state, genotype, benchmark leakage, or reproducibility risks can dominate the result.
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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 nam
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. Secon
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 pr
{
"ranked_hypotheses": [
{
"title": "whether debate-structured causal reasoning improves calibration over direct LLM baselines requires proximal validation",
"description": "The debate supports carrying forward whether debate-structured causal reasoning improves calibration over direct LLM baselines only if a proximal endpoint changes before the late outcome. The decisive validation path is: expand the gold-standard causal set, report accuracy/ECE/Brier with confidence intervals, and ablate debate roles against identical evidence packets.",
"target_gene": "SciDEX",
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Freshness score = exp(-age×ln2/5): halves every 5 years. Green >0.6, Amber 0.3–0.6, Red <0.3.
No citation freshness data yet. Export bibliography — run scripts/audit_citation_freshness.py to populate.
Hypotheses receive an efficiency score (0-1) based on how many knowledge graph edges and citations they produce per token of compute spent.
High-efficiency hypotheses (score >= 0.8) get a price premium in the market, pulling their price toward $0.580.
Low-efficiency hypotheses (score < 0.6) receive a discount, pulling their price toward $0.420.
Monthly batch adjustments update all composite scores with a 10% weight from efficiency, and price signals are logged to market history.
Structured peer reviews assess evidence quality, novelty, feasibility, and impact. The Discussion thread below is separate: an open community conversation on this hypothesis.
No DepMap CRISPR Chronos data found for causal discovery.
Run python3 scripts/backfill_hypothesis_depmap.py to populate.
No curated ClinVar variants loaded for this hypothesis.
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No governance decisions recorded for this hypothesis.
Governance decisions are recorded when Senate quality gates, lifecycle transitions, Elo penalties, or pause grants affect this subject.
No knowledge graph edges recorded
neurodegeneration | 2026-04-27 | complete
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