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AA10715861-00000182-0001  
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Title
Title Causal inference with auxiliary observations  
Kana  
Romanization  
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Title  
Kana  
Romanization  
Creator
Name 太田, 悠太  
Kana オオタ, ユウタ  
Romanization Ota, Yuta  
Affiliation 慶應義塾大学大学院経済学研究科  
Affiliation (Translated) Graduate School of Economics, Keio University  
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Link  

Name 星野, 崇宏  
Kana ホシノ, タカヒロ  
Romanization Hoshino, Takahiro  
Affiliation 慶應義塾大学経済学部; RIKEN AIP  
Affiliation (Translated) Faculty of Economics, Keio University  
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Link  

Name 大津, 泰介  
Kana オオツ, タイスケ  
Romanization Otsu, Taisuke  
Affiliation Department of Economics, London School of Economics; 慶應義塾大学産業研究所  
Affiliation (Translated) Keio Economic Observatory, Keio University  
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Link  
Edition
 
Place
Tokyo  
Publisher
Name Keio Economic Observatory Sangyo Kenkyujo  
Kana  
Romanization  
Date
Issued (from:yyyy) 2024  
Issued (to:yyyy)  
Created (yyyy-mm-dd)  
Updated (yyyy-mm-dd)  
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Physical description
38 p.  
Source Title
Name KEO discussion paper  
Name (Translated)  
Volume  
Issue 182  
Year 2024  
Month 10  
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DOI
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10.14991/004.00000182-0001
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Abstract
Random assignment of treatment and concurrent data collection on treatment and control groups is often impossible in the evaluation of social programs. A standard method for assessing treatment effects in such infeasible situations is to estimate the local average treatment effect under exclusion restriction and monotonicity assumptions. Recently, several studies have proposed methods to estimate the average treatment effect by additionally assuming treatment effects homogeneity across principal strata or conditional independence of assignment and principal strata. However, these assumptions are often difficult to satisfy. We propose a new strategy for nonparametric identification of causal effects that relaxes these assumptions by using auxiliary observations that are readily available in a wide range of settings. Our strategy identifies the average treatment effect for compliers and average treatment effect on treated under only exclusion restrictions and the assumptions on auxiliary observations. The average treatment effect is then identified under relaxed treatment effects homogeneity. We propose sample analog estimators when the assignment is random and multiply robust estimators when the assignment is non-random. We then present details of the GMM estimation and testing methods which utilize overidentified restrictions. The proposed methods are illustrated by empirical examples which revisit the studies by Thornton (2008), Gerber et al. (2009), and Beam (2016), as well as an experimental data related to marketing in a private sector.
 
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英語  
Type of resource
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Technical Report  
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Dec 02, 2024 16:08:04  
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Dec 02, 2024 16:07:28  
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Dec 2, 2024    インデックス を変更
Dec 2, 2024    Creator Name,Creator Kana,Creator Romanization,Creator Affiliation,Creator Affiliation (Translated),Creator Role,Creator Link を変更
 
Index
/ Public / Keio Economic Observatory(KEO) / KEO discussion paper / 101-102, 104-108, 110-119, 121-144, 147-149, 152, 155-156, 158-162, 164-168, 170, 173, 175, 177-179, 181-182, 184-186, 188-191
 
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