Causal Inference in the Presence of Latent Variables and Selection Bias
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
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 ...
-
Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
Introduces Generalized N Factor Model and LGES algorithm that identifies true causal structure including latents up to Markov equivalence class via score-based greedy search.
-
Score-Based Causal Discovery of Latent Variable Causal Models
Introduces score-based causal discovery algorithms for latent variable models that achieve score equivalence and consistency while unifying some existing constraint-based approaches via degrees-of-freedom characterization.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.