Instrumental variable estimation of complier causal treatment effect with interval‐censored data

Biometrics ◽  
2021 ◽  
Author(s):  
Shuwei Li ◽  
Limin Peng
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Interval Censored Data ◽  
Instrumental Variable Estimation ◽  
Interval Censored ◽  
Causal Treatment

The Cox-Aalen model for left-truncated and mixed interval-censored data

Statistics ◽  
2019 ◽  
Vol 53 (5) ◽  
pp. 1152-1167 ◽  
Author(s):  
Pao-sheng Shen ◽  
Li Ning Weng
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Aalen Model ◽  
Interval Censored

Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles H Mullin
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Child Outcomes ◽  
Well Being ◽  
Teenage Mothers ◽  
Age At First Birth ◽  
First Birth ◽  
The Impact

AbstractEmpirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

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