Estimating causal effects with non-experimental data

Non-experimental data;Causal Inference;Survey Weights;Complex Survey Data;Measurement Error;Model Misspecification;ATT;PATT;SATT;ATE;PATE;SATE;Biostatistics

Lenis, DavidChan, Kitty S. ;

Johns Hopkins University

关键词: Non-experimental data;    Causal Inference;    Survey Weights;    Complex Survey Data;    Measurement Error;    Model Misspecification;    ATT;    PATT;    SATT;    ATE;    PATE;    SATE;    Biostatistics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/58620/LENIS-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

In this manuscript we seek to relax some of the traditional assumptions associated with the estimation of causal effects. In particular, we relax the assumption that all confounders are measured without error and the assumption that the observations in the sample are independent and identically distributed. Furthermore, we explore the impact of model misspecification in the estimation of population causal effects.