references.bib

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author = {Pearl, Judea},
file = {:home/silvan/Courses/Thesis/Reading/Books/Pearl - Causality Models Reasoning and Inference.pdf:pdf},
publisher = {Cambridge university press},
title = {{Causality}},
year = {2009}
}
@book{peters2017elements,
author = {Peters, Jonas and Janzing, Dominik and Sch{\"{o}}lkopf, Bernhard},
file = {:home/silvan/Courses/Thesis/Reading/Books/Peters Janzing Schoelkopf - Elements of Causal Inference.pdf:pdf},
publisher = {MIT press},
title = {{Elements of causal inference: foundations and learning algorithms}},
year = {2017}
}
@book{spirtes2000causation,
author = {Spirtes, Peter and Glymour, Clark N and Scheines, Richard and Heckerman, David and Meek, Christopher and Cooper, Gregory and Richardson, Thomas},
file = {:home/silvan/Courses/Thesis/Reading/Books/Spirtes Glymour SCheines - Causation Prediction and Search.pdf:pdf},
publisher = {MIT press},
title = {{Causation, prediction, and search}},
year = {2000}
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@article{kemmeren2014large,
author = {Kemmeren, Patrick and Sameith, Katrin and van de Pasch, Loes A L and Benschop, Joris J and Lenstra, Tineke L and Margaritis, Thanasis and O'Duibhir, Eoghan and Apweiler, Eva and van Wageningen, Sake and Ko, Cheuk W and Others},
file = {:home/silvan/Courses/Thesis/Reading/Data/Kemmeren.pdf:pdf},
journal = {Cell},
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publisher = {Elsevier},
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year = {2014}
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@article{lenstra2011specificity,
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publisher = {Elsevier},
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year = {2011}
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@article{van2010functional,
author = {{Van Wageningen}, Sake and Kemmeren, Patrick and Lijnzaad, Philip and Margaritis, Thanasis and Benschop, Joris J and de Castro, In{\^{e}}s J and {Van Leenen}, Dik and Koerkamp, Marian J A Groot and Ko, Cheuk W and Miles, Antony J and Others},
journal = {Cell},
number = {6},
pages = {991--1004},
publisher = {Elsevier},
title = {{Functional overlap and regulatory links shape genetic interactions between signaling pathways}},
volume = {143},
year = {2010}
}
@article{versteeg2019boosting,
author = {Versteeg, Philip and Mooij, Joris M},
file = {:home/silvan/Courses/Thesis/Reading/Proofread/Boosting Local Causal Discovery in High-Dimensional Expression Data - Versteeg Mooij.pdf:pdf},
journal = {arXiv preprint arXiv:1910.02505},
title = {{Boosting Local Causal Discovery in High-Dimensional Expression Data}},
year = {2019}
}
@book{verma1991equivalence,
author = {Verma, Thomas and Pearl, Judea},
publisher = {UCLA, Computer Science Department},
title = {{Equivalence and synthesis of causal models}},
year = {1991}
}
@article{spirtes1991algorithm,
author = {Spirtes, Peter and Glymour, Clark},
journal = {Social science computer review},
number = {1},
pages = {62--72},
publisher = {Sage Publications Sage CA: Thousand Oaks, CA},
title = {{An algorithm for fast recovery of sparse causal graphs}},
volume = {9},
year = {1991}
}
@article{meinshausen2016methods,
author = {Meinshausen, Nicolai and Hauser, Alain and Mooij, Joris M and Peters, Jonas and Versteeg, Philip and B{\"{u}}hlmann, Peter},
journal = {Proceedings of the National Academy of Sciences},
number = {27},
pages = {7361--7368},
publisher = {National Acad Sciences},
title = {{Methods for causal inference from gene perturbation experiments and validation}},
volume = {113},
year = {2016}
}
@article{chen2007harnessing,
annote = {Trigger LCD},
author = {Chen, Lin S and Emmert-Streib, Frank and Storey, John D},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Chen et al 2007 - Trigger-LCD.pdf:pdf},
journal = {Genome biology},
number = {10},
pages = {R219},
publisher = {BioMed Central},
title = {{Harnessing naturally randomized transcription to infer regulatory relationships among genes}},
volume = {8},
year = {2007}
}
@inproceedings{mooij2015empirical,
annote = {Y-Structures},
author = {Mooij, Joris M and Cremers, Jerome and Others},
booktitle = {UAI 2015 Workshop on Advances in Causal Inference},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Mooij Cremers 2015 - Y-Structures.pdf:pdf},
number = {1504},
pages = {30--39},
title = {{An empirical study of one of the simplest causal prediction algorithms}},
year = {2015}
}
@book{spirtes1999algorithm,
author = {Spirtes, Peter and Meek, Christopher and Richardson, Thomas},
publisher = {MIT Press},
title = {{An algorithm for causal inference in the presence of latent variables and selection bias}},
volume = {1},
year = {1999}
}
@article{claassen2013learning,
author = {Claassen, Tom and Mooij, Joris and Heskes, Tom},
journal = {arXiv preprint arXiv:1309.6824},
title = {{Learning sparse causal models is not NP-hard}},
year = {2013}
}
@article{peters2016causal,
author = {Peters, Jonas and B{\"{u}}hlmann, Peter and Meinshausen, Nicolai},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
number = {5},
pages = {947--1012},
publisher = {Wiley Online Library},
title = {{Causal inference by using invariant prediction: identification and confidence intervals}},
volume = {78},
year = {2016}
}
@article{cooper1997simple,
author = {Cooper, Gregory F},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Cooper 1997 - LCD.pdf:pdf},
journal = {Data Mining and Knowledge Discovery},
number = {2},
pages = {203--224},
publisher = {Springer},
title = {{A simple constraint-based algorithm for efficiently mining observational databases for causal relationships}},
volume = {1},
year = {1997}
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@phdthesis{mani2006bayesian,
author = {Mani, Subramani},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Mani - A BAYESIAN LOCAL CAUSAL DISCOVERYFRAMEWORK - PhD dissertation.pdf:pdf},
school = {University of Pittsburgh},
title = {{A bayesian local causal discovery framework}},
year = {2006}
}
@inproceedings{tian2001causal,
author = {Tian, Jin and Pearl, Judea},
booktitle = {Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Tian and Pearl 2001.pdf:pdf},
organization = {Morgan Kaufmann Publishers Inc.},
pages = {512--521},
title = {{Causal discovery from changes}},
year = {2001}
}
@article{hauser2012characterization,
author = {Hauser, Alain and B{\"{u}}hlmann, Peter},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Hauser Buhlmann 2012.pdf:pdf},
journal = {Journal of Machine Learning Research},
number = {Aug},
pages = {2409--2464},
title = {{Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs}},
volume = {13},
year = {2012}
}
@article{triantafillou2015constraint,
author = {Triantafillou, Sofia and Tsamardinos, Ioannis},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Triantafillou Tsamardinos 2015.pdf:pdf},
journal = {Journal of Machine Learning Research},
number = {2147-2205},
pages = {1},
title = {{Constraint-based causal discovery from multiple interventions over overlapping variable sets.}},
volume = {16},
year = {2015}
}
@inproceedings{cooper1999causal,
author = {Cooper, Gregory F and Yoo, Changwon},
booktitle = {Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Cooper Yoo 1999.pdf:pdf},
organization = {Morgan Kaufmann Publishers Inc.},
pages = {116--125},
title = {{Causal discovery from a mixture of experimental and observational data}},
year = {1999}
}
@article{sachs2005causal,
author = {Sachs, Karen and Perez, Omar and Pe'er, Dana and Lauffenburger, Douglas A and Nolan, Garry P},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Sachs et al 2005.pdf:pdf},
journal = {Science},
number = {5721},
pages = {523--529},
publisher = {American Association for the Advancement of Science},
title = {{Causal protein-signaling networks derived from multiparameter single-cell data}},
volume = {308},
year = {2005}
}
@article{mooij2013cyclic,
author = {Mooij, Joris and Heskes, Tom},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Mooij Heskes 2013.pdf:pdf},
journal = {arXiv preprint arXiv:1309.6849},
title = {{Cyclic causal discovery from continuous equilibrium data}},
year = {2013}
}
@inproceedings{hyttinen2014constraint,
author = {Hyttinen, Antti and Eberhardt, Frederick and J{\"{a}}rvisalo, Matti},
booktitle = {UAI},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Hyttinen et al 2014.pdf:pdf},
pages = {340--349},
title = {{Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.}},
year = {2014}
}
@article{oates2016estimating,
author = {Oates, Chris J and Smith, Jim Q and Mukherjee, Sach},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Oates 2016.pdf:pdf},
journal = {The Journal of Machine Learning Research},
number = {1},
pages = {1880--1903},
publisher = {JMLR. org},
title = {{Estimating causal structure using conditional DAG models}},
volume = {17},
year = {2016}
}
@article{yang2018characterizing,
author = {Yang, Karren D and Katcoff, Abigail and Uhler, Caroline},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Yang et al 2018.pdf:pdf},
journal = {arXiv preprint arXiv:1802.06310},
title = {{Characterizing and learning equivalence classes of causal dags under interventions}},
year = {2018}
}
@article{forre2018constraint,
author = {Forr{\'{e}}, Patrick and Mooij, Joris M},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Forre Mooij 2018.pdf:pdf},
journal = {arXiv preprint arXiv:1807.03024},
title = {{Constraint-based causal discovery for non-linear structural causal models with cycles and latent confounders}},
year = {2018}
}
@article{eberhardt2010combining,
author = {Eberhardt, Frederick and Hoyer, Patrik O and Scheines, Richard and Others},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Eberhardt et al 2010.pdf:pdf},
journal = {Journal of Machine Learning Research},
title = {{Combining experiments to discover linear cyclic models with latent variables}},
year = {2010}
}
@article{hyttinen2012learning,
author = {Hyttinen, Antti and Eberhardt, Frederick and Hoyer, Patrik O},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Hyttinen et al 2012.pdf:pdf},
journal = {Journal of Machine Learning Research},
number = {Nov},
pages = {3387--3439},
title = {{Learning linear cyclic causal models with latent variables}},
volume = {13},
year = {2012}
}
@article{eberhardt2012number,
author = {Eberhardt, Frederick and Glymour, Clark and Scheines, Richard},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Eberhardt et al 2005.pdf:pdf},
journal = {arXiv preprint arXiv:1207.1389},
title = {{On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables}},
year = {2012}
}
@article{hauser2014two,
author = {Hauser, Alain and B{\"{u}}hlmann, Peter},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Hauser Buhlmann 2014.pdf:pdf},
journal = {International Journal of Approximate Reasoning},
number = {4},
pages = {926--939},
publisher = {Elsevier},
title = {{Two optimal strategies for active learning of causal models from interventional data}},
volume = {55},
year = {2014}
}
@article{raskutti2018learning,
annote = {SP},
author = {Raskutti, Garvesh and Uhler, Caroline},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Raskutti Uhler 2018 - SP - Learning directed acyclic graph models based onsparsest permutations.pdf:pdf},
journal = {Stat},
number = {1},
pages = {e183},
publisher = {Wiley Online Library},
title = {{Learning directed acyclic graph models based on sparsest permutations}},
volume = {7},
year = {2018}
}
@inproceedings{wang2017permutation,
author = {Wang, Yuhao and Solus, Liam and Yang, Karren and Uhler, Caroline},
booktitle = {Advances in Neural Information Processing Systems},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/wang et al 2017 - ISP - Permutation-based Causal Inference Algorithmswith Interventions.pdf:pdf},
pages = {5822--5831},
title = {{Permutation-based causal inference algorithms with interventions}},
year = {2017}
}
@article{solus2017consistency,
author = {Solus, Liam and Wang, Yuhao and Matejovicova, Lenka and Uhler, Caroline},
file = {:home/silvan/Courses/Thesis/Reading/Known Intervention Targets/Solus et al 2017.pdf:pdf},
journal = {arXiv preprint arXiv:1702.03530},
title = {{Consistency guarantees for permutation-based causal inference algorithms}},
year = {2017}
}
@inproceedings{magliacane2016ancestral,
author = {Magliacane, Sara and Claassen, Tom and Mooij, Joris M},
booktitle = {Advances in Neural Information Processing Systems},
pages = {4466--4474},
title = {{Ancestral causal inference}},
year = {2016}
}
@article{chickering2002optimal,
author = {Chickering, David Maxwell},
journal = {Journal of machine learning research},
number = {Nov},
pages = {507--554},
title = {{Optimal structure identification with greedy search}},
volume = {3},
year = {2002}
}
@phdthesis{meek1997graphical,
author = {Meek, Christopher},
school = {PhD thesis, Carnegie Mellon University},
title = {{Graphical Models: Selecting causal and statistical models}},
year = {1997}
}
@inproceedings{guruswami2008beating,
author = {Guruswami, Venkatesan and Manokaran, Rajsekar and Raghavendra, Prasad},
booktitle = {2008 49th Annual IEEE Symposium on Foundations of Computer Science},
file = {:home/silvan/Courses/Thesis/Reading/Ordering/Guruswami2008.pdf:pdf},
organization = {IEEE},
pages = {573--582},
title = {{Beating the random ordering is hard: Inapproximability of maximum acyclic subgraph}},
year = {2008}
}
@article{Mani2006,
abstract = {Causal discovery from observational data in the presence of unobserved variables is challenging. Identification of so-called Y substructures is a sufficient condition for ascertaining some causal relations in the large sample limit, without the assumption of no hidden common causes. An example of a Y substructure is A → C, B → C, C → D. This paper describes the first asymptotically reliable and computationally feasible score-based search for discrete Y structures that does not assume that there are no unobserved common causes. For any parameterization of a directed acyclic graph (DAG) that has scores with the property that any DAG that can represent the distribution beats any DAG that can't, and for two DAGs that represent the distribution, if one has fewer parameters than the other, the one with the fewest parameter wins. In this framework there is no need to assign scores to causal structures with unobserved common causes. The paper also describes how the existence of a Y structure shows the presence of an unconfounded causal relation, without assuming that there are no hidden common causes.},
author = {Mani, Subramani and Spirtes, Peter and Cooper, Gregory F.},
file = {:home/silvan/Courses/Thesis/Reading/Other methods/Mani et al 2006 - Y-structures.pdf:pdf},
isbn = {0974903922},
journal = {Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006},
pages = {314--323},
title = {{A theoretical study of Y structures for causal discovery}},
year = {2006}
}
@article{mooij2016joint,
author = {Mooij, Joris M and Magliacane, Sara and Claassen, Tom},
file = {:home/silvan/Courses/Thesis/Reading/Proofread/Joint Causal Inference from Multiple Contexts - Mooij Magliacane Claassen.pdf:pdf},
journal = {arXiv preprint arXiv:1611.10351},
title = {{Joint causal inference from multiple contexts}},
year = {2016}
}

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  year={2017}
}

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  year={2013}
}

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  year={2017},
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}
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  author={Forr{\'e}, Patrick and Mooij, Joris M},
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  year={2017}
}

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@phdthesis{minka2001family,
  title={A family of algorithms for approximate Bayesian inference},
  author={Minka, Thomas Peter},
  year={2001},
  school={Massachusetts Institute of Technology}
}
@article{uhler2013geometry,
  title={Geometry of the faithfulness assumption in causal inference},
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  pages={436--463},
  year={2013},
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}
@article{grace2018will,
  title={When will AI exceed human performance? Evidence from AI experts},
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  year={2019}
}
@article{marcus2018deep,
  title={Deep learning: A critical appraisal},
  author={Marcus, Gary},
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  year={2018}
}
@article{pearl2019seven,
  title={The seven tools of causal inference, with reflections on machine learning},
  author={Pearl, Judea},
  journal={Communications of the ACM},
  volume={62},
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  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{acknowledgements,
  title={On the motivational benefits of friendship and stand-ups: A case study},
  author={Van der Wal, Oskar Douwe and Man, Coco Ka Lok},
  journal={Vansil Journal of Psychology},
  volume={62},
  number={3},
  pages={54--60},
  year={2020},
  publisher={ACM New York, NY, USA}
}