feat(who_should_be_treated): Policy Learning 노트북 추가#2
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feat(who_should_be_treated): Policy Learning 노트북 추가#2
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pypdf·pdfplumber·reportlab 기반 PDF 읽기, 병합, 분할, 폼 작성, OCR 등 전 처리 워크플로를 커버하는 스킬 추가. 참조 문서 및 유틸리티 스크립트 포함. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 4-arm 정책학습 분석 노트북 최초 추가 - DRLearner plug-in / DRPolicyTree / DRPolicyForest 세 방법론 구현 - 불확실 비용(LogNormal) 반영한 Y_net 기반 학습 및 평가 - AIPW score 구성 및 정책 가치 비교 (상수 정책 대비 통계적 유의성 검정) - 섹션 구조: 데이터와 설정 → 탐색과 진단 → 정책학습 방법론 → 정책 비교 → 결론 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 정책 평가 테이블에 95% CI 추가 (ci_lower, ci_upper) - 비용 곡선 셀 복원 (고객별 E[C|X] 추정 + policy_cost_curve) - 통계적 유의성 섹션을 정책 평가에 통합 (별도 헤더 제거) - 처치 배정 분석, 하위집단 효과 차이 섹션 삭제 - 결론 섹션 핵심 수치 중심으로 개선 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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who_should_be_treated_ko.ipynb) 추가Test plan
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