@article{runge2019detecting,
  title={Detecting and quantifying causal associations in large nonlinear time series datasets},
  author={Runge, Jakob and Nowack, Peer and Kretschmer, Marlene and Flaxman, Seth and Sejdinovic, Dino},
  journal={Science advances},
  volume={5},
  number={11},
  pages={eaau4996},
  year={2019},
  publisher={American Association for the Advancement of Science}
}

@inproceedings{runge2020discovering,
  title={Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets},
  author={Runge, Jakob},
  booktitle={Conference on Uncertainty in Artificial Intelligence},
  pages={1388--1397},
  year={2020},
  organization={PMLR}
}

@article{bareinboim2016causal,
  title={Causal inference and the data-fusion problem},
  author={Bareinboim, Elias and Pearl, Judea},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={27},
  pages={7345--7352},
  year={2016},
  publisher={National Acad Sciences}
}
@inproceedings{pearl2011transportability,
  title={Transportability of causal and statistical relations: A formal approach},
  author={Pearl, Judea and Bareinboim, Elias},
  booktitle={Twenty-fifth AAAI conference on artificial intelligence},
  year={2011}
}


@article{gerhardus2020high,
  title={High-recall causal discovery for autocorrelated time series with latent confounders},
  author={Gerhardus, Andreas and Runge, Jakob},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={12615--12625},
  year={2020}
}

@article{wagener2007catchment,
  title={Catchment classification and hydrologic similarity},
  author={Wagener, Thorsten and Sivapalan, Murugesu and Troch, Peter and Woods, Ross},
  journal={Geography compass},
  volume={1},
  number={4},
  pages={901--931},
  year={2007},
  publisher={Wiley Online Library}
}


@article{runge2018causal,
  title={Causal network reconstruction from time series: From theoretical assumptions to practical estimation},
  author={Runge, Jakob},
  journal={Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume={28},
  number={7},
  pages={075310},
  year={2018},
  publisher={AIP Publishing LLC}
}

@article{runge2019inferring,
  title={Inferring causation from time series in Earth system sciences},
  author={Runge, Jakob and Bathiany, Sebastian and Bollt, Erik and Camps-Valls, Gustau and Coumou, Dim and Deyle, Ethan and Glymour, Clark and Kretschmer, Marlene and Mahecha, Miguel D and Mu{\~n}oz-Mar{\'\i}, Jordi and others},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1--13},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{mooij2020joint,
  title={Joint causal inference from multiple contexts},
  author={Mooij, Joris M and Magliacane, Sara and Claassen, Tom},
  journal={The Journal of Machine Learning Research},
  volume={21},
  number={1},
  pages={3919--4026},
  year={2020},
  publisher={JMLRORG}
}

@book{spirtes2000causation,
  title={Causation, prediction, and search},
  author={Spirtes, Peter and Glymour, Clark N and Scheines, Richard and Heckerman, David},
  year={2000},
  publisher={MIT press}
}

@article{pearl1993comment,
 ISSN = {08834237},
 URL = {http://www.jstor.org/stable/2245965},
 author = {Judea Pearl},
 journal = {Statistical Science},
 number = {3},
 pages = {266--269},
 publisher = {Institute of Mathematical Statistics},
 title = {[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention},
 urldate = {2023-02-03},
 volume = {8},
 year = {1993}
}

@article{chickering2002learning,
  title={Learning equivalence classes of Bayesian-network structures},
  author={Chickering, David Maxwell},
  journal={The Journal of Machine Learning Research},
  volume={2},
  pages={445--498},
  year={2002},
  publisher={JMLR. org}
}
@inproceedings{spirtes2016causal,
  title={Causal discovery and inference: concepts and recent methodological advances},
  author={Spirtes, Peter and Zhang, Kun},
  booktitle={Applied informatics},
  volume={3},
  pages={1--28},
  year={2016},
  organization={SpringerOpen}
}
@article{entner2010causal,
  title={On causal discovery from time series data using FCI},
  author={Entner, Doris and Hoyer, Patrik O},
  journal={Probabilistic graphical models},
  pages={121--128},
  year={2010}
}

@book{angrist2009mostly,
  title={Mostly harmless econometrics: An empiricist's companion},
  author={Angrist, Joshua D and Pischke, J{\"o}rn-Steffen},
  year={2009},
  publisher={Princeton university press}
}


@article{dawid2002influence,
  title={Influence diagrams for causal modelling and inference},
  author={Dawid, A Philip},
  journal={International Statistical Review},
  volume={70},
  number={2},
  pages={161--189},
  year={2002},
  publisher={Wiley Online Library}
}

@article{bareinboim2013general,
  title={A general algorithm for deciding transportability of experimental results},
  author={Bareinboim, Elias and Pearl, Judea},
  journal={Journal of causal Inference},
  volume={1},
  number={1},
  pages={107--134},
  year={2013},
  publisher={De Gruyter}
}

@article{peters2016causal,
  title={Causal inference by using invariant prediction: identification and confidence intervals},
  author={Peters, Jonas and B{\"u}hlmann, Peter and Meinshausen, Nicolai},
  journal={Journal of the Royal Statistical Society. Series B (Statistical Methodology)},
  pages={947--1012},
  year={2016},
  publisher={JSTOR}
}

@article{huang2020causal,
  title={Causal discovery from heterogeneous/nonstationary data},
  author={Huang, Biwei and Zhang, Kun and Zhang, Jiji and Ramsey, Joseph and Sanchez-Romero, Ruben and Glymour, Clark and Sch{\"o}lkopf, Bernhard},
  journal={The Journal of Machine Learning Research},
  volume={21},
  number={1},
  pages={3482--3534},
  year={2020},
  publisher={JMLRORG}
}

@article{nickell1981biases,
  title={Biases in dynamic models with fixed effects},
  author={Nickell, Stephen},
  journal={Econometrica: Journal of the econometric society},
  pages={1417--1426},
  year={1981},
  publisher={JSTOR}
}

@book{peters2017elements,
  title={Elements of causal inference: foundations and learning algorithms},
  author={Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
  year={2017},
  publisher={The MIT Press}
}

\@Book{pearl:2k,
  author =   "Pearl, J.",
  title =    "Causality: Models, Reasoning, and Inference",
  year =     2000,
  publisher =    "Cambridge University Press",
  address =      "New York",
  note =     "2nd edition, 2009"

}

@article{granger1969investigating,
  title={Investigating causal relations by econometric models and cross-spectral methods},
  author={Granger, Clive WJ},
  journal={Econometrica: journal of the Econometric Society},
  pages={424--438},
  year={1969},
  publisher={JSTOR}
}

@article{cooper1997LCD,
author = {Cooper, Gregory F.},
title = {A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships},
year = {1997},
issue_date = {1997},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {2},
issn = {1384-5810},
url = {https://doi.org/10.1023/A:1009787925236},
doi = {10.1023/A:1009787925236},
abstract = {This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.},
journal = {Data Min. Knowl. Discov.},
month = {jan},
pages = {203–224},
numpages = {22},
keywords = {data mining, causal discovery, observational data}
}

@inproceedings{meek1995causal,
    author = {Meek, Christopher},
    title = {Causal Inference and Causal Explanation with Background Knowledge},
    year = {1995}, isbn = {1558603859},
    publisher = {Morgan Kaufmann Publishers Inc.}, address = {San Francisco, CA, USA},
    abstract = {This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation consistent with a set of background knowledge which explains all of the observed independence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such causal explanation?}, booktitle = {Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence},
    pages = {403–410},
    numpages = {8},
    location = {Montr\'{e}al, Qu\'{e}, Canada},
    series = {UAI'95}
}

@inproceedings{enter2010causal,
  title={On Causal Discovery from Time Series Data using FCI},
  author={Entner, Doris and Hoyer, Patrik O.},
  booktitle = {Proceedings of the 5th European Workshop on Probabilistic Graphical Models},
  editor = {P. Myllym{\"a}ki and T. Roos and T. Jaakkola},
  pages={121--128},
  year={2010},
  publisher={Helsinki Institute for Information Technology HIIT},
  address = {Helsinki, FI}
}

@inproceedings{malinsky2018causal,
  title =    {Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding},
  author =   {Daniel Malinsky and Peter Spirtes},
  booktitle =    {Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery},
  editor = {Le, T D and Zhang, K and K{\i}c{\i}man, E and Hyv\"{a}rinen, A and Liu, Lin},
  pages =    {23--47},
  year =   {2018},
  volume =   {92},
  series =   {Proceedings of Machine Learning Research},
  address =    {London, UK},
  month =    {20 Aug},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v92/malinsky18a/malinsky18a.pdf},
  url =    {http://proceedings.mlr.press/v92/malinsky18a.html},
  abstract =   {We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.}
}


@InProceedings{pmlr-v115-jensen20a,
  title = 	 {Object Conditioning for Causal Inference},
  author =       {Jensen, David and Burroni, Javier and Rattigan, Matthew},
  booktitle = 	 {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference},
  pages = 	 {1072--1082},
  year = 	 {2020},
  editor = 	 {Adams, Ryan P. and Gogate, Vibhav},
  volume = 	 {115},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {22--25 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v115/jensen20a/jensen20a.pdf},
  url = 	 {https://proceedings.mlr.press/v115/jensen20a.html},
  abstract = 	 {We describe and analyze a form of conditioning that is widely applied within social science and applied statistics but that is virtually unknown within causal graphical models. This approach, which we term object conditioning, can adjust for the effects of latent confounders and yet avoid the pitfall of conditioning on colliders. We describe object conditioning using plate models and show how its probabilistic implications can be explained using the property of exchangeability. We show that several seemingly obvious interpretations of object conditioning are insufficient to describe its probabilistic implications.  Finally, we use object conditioning to describe and unify key aspects of a diverse set of techniques for causal inference, including within-subjects designs, difference-in-differences designs, and interrupted time-series designs.}
}

@inproceedings{witty2020causal,
  title={Causal inference using Gaussian processes with structured latent confounders},
  author={Witty, Sam and Takatsu, Kenta and Jensen, David and Mansinghka, Vikash},
  booktitle={International Conference on Machine Learning},
  pages={10313--10323},
  year={2020},
  organization={PMLR}
}

@article{daniusis2012inferring,
  title={Inferring deterministic causal relations},
  author={Daniusis, Povilas and Janzing, Dominik and Mooij, Joris and Zscheischler, Jakob and Steudel, Bastian and Zhang, Kun and Sch{\"o}lkopf, Bernhard},
  journal={arXiv preprint arXiv:1203.3475},
  year={2012}
}

@inproceedings{lemeire2011inferring,
  title={Inferring the causal decomposition under the presence of deterministic relations.},
  author={Lemeire, Jan and Meganck, Stijn and Cartella, Francesco and Liu, Tingting and Statnikov, Alexander R},
  booktitle={ESANN},
  year={2011}
}

@article{lemeire2012conservative,
  title={Conservative independence-based causal structure learning in absence of adjacency faithfulness},
  author={Lemeire, Jan and Meganck, Stijn and Cartella, Francesco and Liu, Tingting},
  journal={International Journal of Approximate Reasoning},
  volume={53},
  number={9},
  pages={1305--1325},
  year={2012},
  publisher={Elsevier}
}

@article{geiger1990identifying,
  title={Identifying independence in Bayesian networks},
  author={Geiger, Dan and Verma, Thomas and Pearl, Judea},
  journal={Networks},
  volume={20},
  number={5},
  pages={507--534},
  year={1990},
  publisher={Wiley Online Library}
}

@inproceedings{assaad2022discovery,
  title={Discovery of extended summary graphs in time series},
  author={Assaad, Charles K and Devijver, Emilie and Gaussier, Eric},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={96--106},
  year={2022},
  organization={PMLR}
}

@inproceedings{mooij2020constraint,
  title={Constraint-based causal discovery using partial ancestral graphs in the presence of cycles},
  author={Mooij, Joris M and Claassen, Tom},
  booktitle={Conference on Uncertainty in Artificial Intelligence},
  pages={1159--1168},
  year={2020},
  organization={PMLR}
}

@article{bongers2021foundations,
author = {Stephan Bongers and Patrick Forr{\'e} and Jonas Peters and Joris M. Mooij},
title = {{Foundations of structural causal models with cycles and latent variables}},
volume = {49},
journal = {The Annals of Statistics},
number = {5},
publisher = {Institute of Mathematical Statistics},
pages = {2885 -- 2915},
keywords = {causal graph, counterfactuals, cycles, interventions, marginalization, Markov properties, solvability, Structural causal models},
year = {2021},
doi = {10.1214/21-AOS2064},
URL = {https://doi.org/10.1214/21-AOS2064}
}

@article{reisach2021beware,
  title={Beware of the simulated dag! causal discovery benchmarks may be easy to game},
  author={Reisach, Alexander and Seiler, Christof and Weichwald, Sebastian},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={27772--27784},
  year={2021}
}

@inproceedings{andrews2020completeness,
  title={On the completeness of causal discovery in the presence of latent confounding with tiered background knowledge},
  author={Andrews, Bryan and Spirtes, Peter and Cooper, Gregory F},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={4002--4011},
  year={2020},
  organization={PMLR}
}

@article{saggioro2020reconstructing,
  title={Reconstructing regime-dependent causal relationships from observational time series},
  author={Saggioro, Elena and de Wiljes, Jana and Kretschmer, Marlene and Runge, Jakob},
  journal={Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume={30},
  number={11},
  pages={113115},
  year={2020},
  publisher={AIP Publishing LLC}
}

@inproceedings{zhou2022causal,
  title={Causal discovery with heterogeneous observational data},
  author={Zhou, Fangting and He, Kejun and Ni, Yang},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={2383--2393},
  year={2022},
  organization={PMLR}
}

@inproceedings{zhang2011kernel,
  title={Kernel-based Conditional Independence Test and Application in Causal Discovery},
  author={Zhang, K and Peters, J and Janzing, D and Sch{\"o}lkopf, B},
  booktitle={27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)},
  pages={804--813},
  year={2011},
  organization={AUAI Press}
}

@book{sheskin2020handbook,
  title={Handbook of parametric and nonparametric statistical procedures},
  author={Sheskin, David J},
  year={2020},
  publisher={crc Press}
}

