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+---
+id: wiki-2026-0508-etiology-of-disease
+title: Etiology of Disease
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Disease Causation, Pathogenesis, Causal Inference (Medicine)]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [medicine, epidemiology, causal-inference, pathology]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: r
+  framework: epidemiology
+---
+
+# Etiology of Disease
+
+## 매 한 줄
+> **"매 disease 의 cause = single agent 가 아닌 web of necessary + sufficient + component causes"**. 매 1840 Henle-Koch 의 single-pathogen postulate → 매 Rothman 1976 sufficient-component model → 매 2026 의 multi-omics + Mendelian randomization + DAG-based causal inference.
+
+## 매 핵심
+
+### 매 causation models
+- **Henle-Koch postulates**: 매 isolation, transmission, re-isolation — 매 monocausal infectious era.
+- **Bradford Hill criteria (1965)**: 9 viewpoints — strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, analogy.
+- **Rothman sufficient-component**: 매 disease = sum of "pies", each pie = sufficient cause = set of component causes. 매 same disease 의 multiple sufficient sets.
+- **Counterfactual / DAG**: 매 Pearl 의 do-calculus, 매 confounder identification.
+
+### 매 causal categories
+- **Necessary**: 매 cause 없이 disease 없음 (예: HIV → AIDS).
+- **Sufficient**: 매 cause 만 으로 disease (rare in practice).
+- **Component**: 매 sufficient cause 의 part (예: 흡연 + asbestos + genetic).
+- **Risk factor**: 매 association 만 — causality 의 unconfirmed.
+
+### 매 응용
+1. Smoking → lung cancer (Doll & Hill 1950).
+2. H. pylori → peptic ulcer (Marshall 1984).
+3. HPV → cervical cancer (zur Hausen 2008 Nobel).
+4. APOE4 → Alzheimer (genetic risk, not deterministic).
+
+## 💻 패턴
+
+### Bradford Hill scoring
+```python
+from dataclasses import dataclass
+
+@dataclass
+class HillCriteria:
+    strength: float          # RR or OR
+    consistency: int         # # of confirming studies
+    temporality: bool        # exposure precedes outcome
+    gradient: bool           # dose-response
+    plausibility: bool       # mechanism known
+    coherence: bool          # fits prior knowledge
+    experiment: bool         # RCT / natural experiment
+    specificity: bool
+
+def hill_score(c: HillCriteria) -> int:
+    score = 0
+    score += 2 if c.strength >= 3 else 1 if c.strength >= 2 else 0
+    score += min(c.consistency // 3, 3)
+    score += [c.temporality, c.gradient, c.plausibility,
+              c.coherence, c.experiment, c.specificity].count(True)
+    return score  # ≥7 = strong causal evidence
+```
+
+### Confounder adjustment via DAG (DoWhy)
+```python
+import dowhy
+from dowhy import CausalModel
+
+model = CausalModel(
+    data=df,
+    treatment="smoking",
+    outcome="lung_cancer",
+    common_causes=["age", "sex", "ses"],
+    instruments=["tobacco_tax"],
+)
+identified = model.identify_effect()
+estimate = model.estimate_effect(identified, method_name="backdoor.linear_regression")
+refute = model.refute_estimate(identified, estimate, method_name="placebo_treatment_refuter")
+```
+
+### Mendelian randomization
+```python
+# Instrumental variable: SNP → exposure → outcome
+# (SNP independent of confounders)
+import statsmodels.api as sm
+
+# Wald ratio: beta_outcome / beta_exposure
+def mendelian_ratio(snp_exposure_beta: float, snp_outcome_beta: float) -> float:
+    return snp_outcome_beta / snp_exposure_beta
+```
+
+### Population attributable fraction
+```python
+def paf(prevalence: float, relative_risk: float) -> float:
+    """Fraction of disease attributable to exposure in population."""
+    return prevalence * (relative_risk - 1) / (1 + prevalence * (relative_risk - 1))
+
+# Smoking prevalence 25%, RR for lung cancer 20:
+print(paf(0.25, 20))  # ~0.83 → 83% of lung cancer attributable to smoking
+```
+
+### Sufficient-component pie visualization
+```python
+def sufficient_pies(disease: str) -> list[set[str]]:
+    """Each pie = a set of component causes that together suffice."""
+    return [
+        {"smoking", "genetic_susceptibility"},      # pie 1
+        {"asbestos", "smoking"},                     # pie 2
+        {"radon", "smoking", "vitamin_deficiency"},  # pie 3
+    ]
+```
+
+## 매 결정 기준
+| 상황 | Method |
+|---|---|
+| Single pathogen, acute | Koch postulates (modernized) |
+| Chronic, multifactorial | Bradford Hill + Rothman |
+| Observational with confounders | DAG + backdoor adjustment |
+| Genetic causation suspected | Mendelian randomization |
+| RCT impossible (ethics) | quasi-experiment + sensitivity analysis |
+
+**기본값**: 매 Bradford Hill + DAG-based confounder adjustment + sensitivity analysis (E-value).
+
+## 🔗 Graph
+- 부모: [[Causal Inference]]
+
+## 🤖 LLM 활용
+**언제**: 매 literature synthesis 의 mechanism aggregation, 매 DAG 의 candidate confounder enumeration, 매 sufficient-component 의 component proposal.
+**언제 X**: 매 final causation claim 의 LLM 의 의존 — 매 effect estimate 의 source data + statistical method 의 검증 필수.
+
+## ❌ 안티패턴
+- **Single-cause thinking**: 매 multifactorial disease 의 monocausal explanation — 매 H. pylori 발견 전 의 stress 의 ulcer 의 단일 cause 의 오해.
+- **Correlation = causation**: 매 RR 만 으로 causal claim — 매 confounding, reverse causation, selection bias 의 무시.
+- **Ignoring temporality**: 매 cross-sectional study 의 causal direction 의 결정 X.
+- **Hill criteria 의 checklist 화**: 매 의 mechanical scoring — 매 viewpoints, not rules (Hill 의 의도).
+
+## 🧪 검증 / 중복
+- Verified (Rothman & Greenland "Modern Epidemiology" 4th ed, Hernán & Robins "Causal Inference: What If" 2024, Pearl "Causality" 2nd ed).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Hill criteria, Rothman pies, DAG/MR patterns 추가 |