{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.18535",
  "title": "Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders",
  "summary": "This method replaces standard DAG-learning approaches that assume no hidden confounding, recovering the observed-variable causal graph by decomposing the precision matrix into a sparse component (observed conditional dependencies) plus a low-rank component (a few latent confounders). It costs only classical convex-optimization compute, easily within a 24 GB GPU or CPU budget, but requires roughly max{s log p, rp} samples and linear structural equation assumptions to guarantee identifiability. It can fail when relationships are nonlinear, latent confounders are numerous enough to break the low-rank assumption, or sample size falls below the theoretical threshold, yielding incorrect edge directions.",
  "rationale": "The reader adopts production inference and training techniques, and this is a theory-oriented causal discovery algorithm with no quantified efficiency or quality gain relevant to their constrained-infrastructure production decisions.",
  "technique": "Sparse-plus-low-rank precision matrix causal discovery",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18535",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This method replaces standard DAG-learning approaches that assume no hidden confounding, recovering the observed-variable causal graph by decomposing the precision matrix into a sparse component (observed conditional dependencies) plus a low-rank component (a few latent confounders). It costs only classical convex-optimization compute, easily within a 24 GB GPU or CPU budget, but requires roughly max{s log p, rp} samples and linear structural equation assumptions to guarantee identifiability. It can fail when relationships are nonlinear, latent confounders are numerous enough to break the low-rank assumption, or sample size falls below the theoretical threshold, yielding incorrect edge directions.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18535",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "next_check": "",
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      "dimension": "operations",
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    },
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      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}