Corpus ID: 10269018

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

@inproceedings{Zhang2016OnTI,
  title={On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection},
  author={K. Zhang and J. Zhang and Biwei Huang and B. Sch{\"o}lkopf and C. Glymour},
  booktitle={UAI},
  year={2016}
}
We study the identifiability and estimation of functional causal models under selection bias, with a focus on the situation where the selection depends solely on the effect variable, which is known as outcome-dependent selection. We address two questions of identifiability: the identifiability of the causal direction between two variables in the presence of selection bias, and, given the causal direction, the identifiability of the model with outcome-dependent selection. Regarding the first, we… Expand
View Paper
auai.org
8 Citations
Background Citations
5
Methods Citations
3

8 Citations

Citation Type

Citation Type

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
  • 4
  • PDF
Learning the Causal Structure of Copula Models with Latent Variables
  • 3
  • PDF
Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data
  • 4
Study on : Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables
  • PDF
Review of Causal Discovery Methods Based on Graphical Models
  • 77
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
  • 6
  • PDF
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
  • 7
  • PDF
Causal Discovery from Mixed Data using Gaussian Copula Models

References

SHOWING 1-10 OF 23 REFERENCES
On the Identifiability of the Post-Nonlinear Causal Model
  • 273
  • Highly Influential
  • PDF
Recovering from Selection Bias using Marginal Structure in Discrete Models
  • 5
  • PDF
A Linear Non-Gaussian Acyclic Model for Causal Discovery
  • 774
  • Highly Influential
  • PDF
Causal Inference in the Presence of Latent Variables and Selection Bias
  • 245
  • PDF
Graphical models for inference under outcome-dependent sampling
  • 50
  • PDF
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
  • J. Zhang
  • Mathematics, Computer Science
  • Artif. Intell.
  • 2008
  • 239
Nonlinear causal discovery with additive noise models
  • 531
  • PDF
Models for Sample Selection Bias
  • 700
  • PDF
Recovering from Selection Bias in Causal and Statistical Inference
  • 82
  • Highly Influential
  • PDF
Controlling Selection Bias in Causal Inference
  • 95
  • PDF
...
1
2
3
...