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What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment.
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Expression profiling reveals off-target gene regulation by RNAi
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Counterfactuals, hypotheticals and potential responses: a philosophical examination of statistical causality
A. P. Dawid · 2006
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Direct and indirect effects of sequential treatments
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DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
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The Decision-Theoretic Approach to Causal Inference
A. P. Dawid · 2012
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Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
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Learning linear cyclic causal models with latent variables
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Information-geometric approach to inferring causal directions
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On causal and anticausal learning
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Controlling false positive selections in high-dimensional regression and causal inference
P. Bühlmann, P. Rütimann, and M. Kalisch · 2013
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Testing equality of functions under monotonicity constraints
C. Durot, P. Groeneboom, and H. Lopuhaä · 2013
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Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality
T. Richardson and J. M. Robins · 2013
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CAM: Causal additive models, high-dimensional order search and penalized regression
P. Bühlmann, J. Peters, and J. Ernest · 2014
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Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors
P. Kemmeren, K. Sameith, L. A. van de Pasch, J. J. Benschop, T. L. Lenstra, T. Margaritis, E. O’Duibhir, E. Apweiler, S. van Wageningen, C. W. Ko, S. van Heesch, M. M. Kashani, G. Ampatziadis-Michailidis, M. O. Brok, N. A. Brabers, A. J. Miles, D. Bouwmeester, S. R. van Hooff, H. van Bakel, E. Sluiters, L. V. Bakker, B. Snel, P. Lijnzaad, D. van Leenen, M. J. Groot Koerkamp, and F. C. Holstege · 2014
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Identifiability of Gaussian structural equation models with equal error variances
J. Peters and P. Bühlmann · 2014
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Causal discovery with continuous additive noise models
J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf · 2014
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R: A Language and Environment for Statistical Computing
R Core Team · 2014
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Statistical causality from a decision-theoretic perspective
A. P. Dawid · 2015
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Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs
A. Hauser and P. Bühlmann · 2015
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Instrumental variables estimation with some invalid instruments and its application to mendelian randomization
H. Kang, A. Zhang, T. Cai, and D.S. Small · 2015
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backShift: Learning causal cyclic graphs from unknown shift interventions
D. Rothenhäusler, C. Heinze, J. Peters, and N. Meinshausen · 2015
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