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NYC TLC 2023 HVFHS 데이터(5M row reservoir sample)에 FDC를 적용하여 공유 탑승 승인(X) → 팁(Y) 인과효과를 추정하는 노트북 추가. - 세 가지 내생성 원인(요금·시공간·교란편향) 식별 전략 및 DAG 시각화 - OD×2h 고정효과 demeaning + 2단계 OLS + Delta method SE - Naive OLS vs FDC 비교 차트 (교란편향 제거 효과 시각화) - One-sided noncompliance → ATT 해석, Bootstrap SE 검증 - 샘플: HuggingFace reservoir sample(seed=42) 사용으로 날짜 편향 제거 - duckdb, dowhy 의존성 추가 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- frontdoor_criterion.ipynb → _ko / _en 분리 - 챕터 제목: "When Confounders Hide | Front-Door Criterion" - 언어 스위처 추가 (한↔영) - 노트북 내용 전면 polishing (가독성, 논리, 중복 제거) - why_causal_inference: 제목 셀 분리, 섹션 번호 제거 - linearmodels 의존성 추가 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Summary
_ko) / 영어(_en)로 분리하여 추가why_causal_inference노트북 제목 셀 분리 및 섹션 번호 제거linearmodels의존성 추가Test plan
frontdoor_criterion_en.ipynb) 처음부터 끝까지 실행 확인frontdoor_criterion_ko.ipynb) 처음부터 끝까지 실행 확인🤖 Generated with Claude Code