Logic of Causal Inference from Data Under Presence of Latent Confounders

被引：0

作者：

O. S. Balabanov

机构：

[1] National Academy of Sciences of Ukraine,Institute of Software Systems

来源：

Cybernetics and Systems Analysis | 2022年 / 58卷

关键词：

causal network; d-separation; conditional independence; edge orientation rules; confounder; collider; illusory edge; dependence testability assumptions;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The problems of causal inference of models from empirical data (by independence-based methods) and some error mechanisms are examined. We demonstrate that the known rules for orienting edges of model can produce misleading results under presence of latent confounders. We propose corrections to the orientation rules aiming to successfully extend them for inference of models beyond the ancestral model class. The necessary assumptions justifying the inference of adequate causal relationships from data are suggested.

引用

页码：171 / 185

页数：14

共 50 条

[1] Logic of Causal Inference from Data Under Presence of Latent Confounders
Balabanov, O. S.
CYBERNETICS AND SYSTEMS ANALYSIS, 2022, 58 (02) : 171 - 185
[2] Causal Discovery from Markov Properties Under Latent Confounders
Balabanov, O. S.
CYBERNETICS AND SYSTEMS ANALYSIS, 2024, 60 (03) : 359 - 374
[3] Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Cheng, Debo
Xu, Ziqi
Li, Jiuyong
Liu, Lin
Liu, Jixue
Gao, Wentao
Le, Thuc Duy
THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 10, 2024, : 11480 - 11488
[4] Causal inference in survival analysis under deterministic missingness of confounders in register data
Ciocanea-Teodorescu, Iuliana
Goetghebeur, Els
Waernbaum, Ingeborg
Schon, Staffan
Gabriel, Erin E.
STATISTICS IN MEDICINE, 2023, : 1946 - 1964
[5] Network inference in the presence of latent confounders: The role of instantaneous causalities
Elsegai, Heba
Shiells, Helen
Thiel, Marco
Schelter, Bjoern
JOURNAL OF NEUROSCIENCE METHODS, 2015, 245 : 91 - 106
[6] Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
Forre, Patrick
Mooij, Joris M.
35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), 2020, 115 : 71 - 80
[7] Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
Rohekar, Raanan Y.
Nisimov, Shami
Gurwicz, Yaniv
Novik, Gal
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
[8] Causal inference with confounders missing not at random
Yang, S.
Wang, L.
Ding, P.
BIOMETRIKA, 2019, 106 (04) : 875 - 888
[9] On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
Zhang, Jiji
ARTIFICIAL INTELLIGENCE, 2008, 172 (16-17) : 1873 - 1896
[10] Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data
Wang, Lingfei
Michoel, Tom
PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (08) : e1005703

← 1 2 3 4 5 →