.png){kind=icon}
返回
Graphical Causal Models for Survey Inference
.png){kind=icon}
OA
AI
DOI：10.1177/00491241231176851.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of bias and assumptions for eliminating it in various selection scenarios. We then introduce and analyze graphical representations of multiple selection stages in the data collection process, and highlight the strong assumptions implicit in using only design weights. Furthermore, we show that the common practice of selecting adjustment variables based on correlations with sample selection and outcome variables of interest is ill-justified and that nonresponse weighting when the interest is in causal inference may come at severe costs. Finally, we identify further areas for survey methodology research that can benefit from advances in causal graph theory.
Keyword:
nonresponse
DAG
causal graphs
sample selection
statistical inference
causal inference
surveys
期刊
S
IF:
6.5
论文数:
1.2K
被引数:
8.6K
机构
引用论文
CAUSAL DIAGRAMS FOR TREATMENT EFFECT ESTIMATION WITH APPLICATION TO EFFICIENT COVARIATE SELECTION
PREAI
分享收藏Simulation of stably stratified flow in complex terrain: flow structures and dividing streamline
PREAI
分享收藏Collider bias undermines our understanding of COVID-19 disease risk and severity对撞机偏见破坏了我们对新型冠状病毒肺炎疾病风险和严重程度的理解.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏
查看更多内容