{
  "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": "2610.20893",
  "title": "Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes",
  "summary": "PSB replaces parametric bridge models in proximal causal inference for right-censored time-to-event outcomes with flexible kernel (PSB-K) and neural (PSB-N) bridge learners offering doubly robust estimation of survival curves. The cost is substantial statistical engineering: it requires valid proxy variables for unmeasured confounding, careful bridge-function estimation, and specialized survival-analysis expertise rather than any GPU or serving infrastructure. It can fail when proxies are weakened or invalid, under heavy censoring, and its benchmark advantages are dataset-dependent (PSB-K wins on SEM/ACTG, PSB-N on Framingham), so no estimator is uniformly reliable.",
  "rationale": "This is a causal-inference method for observational survival data, not a technique applicable to building, training, or serving ML models under the reader's infrastructure constraints.",
  "technique": "Doubly robust proximal causal inference for censored survival outcomes",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20893",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PSB replaces parametric bridge models in proximal causal inference for right-censored time-to-event outcomes with flexible kernel (PSB-K) and neural (PSB-N) bridge learners offering doubly robust estimation of survival curves. The cost is substantial statistical engineering: it requires valid proxy variables for unmeasured confounding, careful bridge-function estimation, and specialized survival-analysis expertise rather than any GPU or serving infrastructure. It can fail when proxies are weakened or invalid, under heavy censoring, and its benchmark advantages are dataset-dependent (PSB-K wins on SEM/ACTG, PSB-N on Framingham), so no estimator is uniformly reliable.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20893",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": "",
  "exposures_source_sha256": ""
}