An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
arXiv preprint arXiv:1302.4983 , year=
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abstract
We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Necessary and sufficient conditions for ATE identifiability under selection bias using weaker assumptions on probability classes than prior graphical criteria.
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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Towards a holistic understanding of Selection Bias for Causal Effect Identification
Necessary and sufficient conditions for ATE identifiability under selection bias using weaker assumptions on probability classes than prior graphical criteria.