Methods for causal inference from gene perturbation experiments and validation
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Methods for causal inference from gene perturbation experiments and validation. / Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter.
I: Proceedings of the National Academy of Sciences of the United States of America, Bind 113, Nr. 27, 05.07.2016, s. 7361-7368.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Methods for causal inference from gene perturbation experiments and validation
AU - Meinshausen, Nicolai
AU - Hauser, Alain
AU - Mooij, Joris M
AU - Peters, Jonas
AU - Versteeg, Philip
AU - Bühlmann, Peter
PY - 2016/7/5
Y1 - 2016/7/5
N2 - Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.
AB - Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.
KW - Journal Article
U2 - 10.1073/pnas.1510493113
DO - 10.1073/pnas.1510493113
M3 - Journal article
C2 - 27382150
VL - 113
SP - 7361
EP - 7368
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 27
ER -
ID: 165942082