* [151] M. Drees, R.M. Godbole and P. Roy, _Theory and Phenomenology of Sparticles: An account of four-dimensional N=1 supersymmetry in High Energy Physics_ , World Scientific Publishing Co. Pte. Ltd. (2004).
  * [152] M.E. Peskin and D.V. Schroeder, _An Introduction to Quantum Field Theory_ , Addison-Wesley, Reading, USA (1995).
  * [153] A. Denner, _Techniques for calculation of electroweak radiative corrections at the one loop level and results for W physics at LEP-200_ , _Fortsch. Phys._ 41 (1993) 307 [0709.1075].
  * [154] A. Bharucha, A. Fowler, G. Moortgat-Pick and G. Weiglein, _Consistent on shell renormalisation of electroweakinos in the complex MSSM: LHC and LC predictions_ , _JHEP_ 05 (2013) 053 [1211.3134].
  * [155] G. Degrassi, P. Gambino and P.P. Giardino, _The $m_{\scriptscriptstyle W}-m_{\scriptscriptstyle Z}$ interdependence in the Standard Model: a new scrutiny_, _JHEP_ 05 (2015) 154 [1411.7040].
  * [156] S. Bauberger, _Two-loop contributions to muon decay_ , Ph.D. thesis, Würzburg U., 1997.
  * [157] A. Freitas, W. Hollik, W. Walter and G. Weiglein, _Electroweak two loop corrections to the $M_{W}-M_{Z}$ mass correlation in the standard model_, _Nucl. Phys. B_ 632 (2002) 189 [hep-ph/0202131].
  * [158] M. Awramik, M. Czakon, A. Onishchenko and O. Veretin, _Bosonic corrections to Delta r at the two loop level_ , _Phys. Rev. D_ 68 (2003) 053004 [hep-ph/0209084].
  * [159] S. Dittmaier, _Electric charge renormalization to all orders_ , _Phys. Rev. D_ 103 (2021) 053006 [2101.05154].
  * [160] E. Fuchs and G. Weiglein, _Breit-Wigner approximation for propagators of mixed unstable states_ , _JHEP_ 09 (2017) 079 [1610.06193].
  * [161] S. Hessenberger, _Two-loop corrections to electroweak precision observables in Two-Higgs-Doublet-Models_ , Ph.D. thesis, Munich, Tech. U., 2018\.
  * [162] A. Freitas and D. Stöckinger, _Gauge dependence and renormalization of tan beta_ , in _10th International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY02)_ , pp. 657–661, 10, 2002 [hep-ph/0210372].
  * [163] A. Freitas and D. Stöckinger, _Gauge dependence and renormalization of tan beta in the MSSM_ , _Phys. Rev. D_ 66 (2002) 095014 [hep-ph/0205281].
  * [164] N. Baro, F. Boudjema and A. Semenov, _Automatised full one-loop renormalisation of the MSSM. I. The Higgs sector, the issue of tan(beta) and gauge invariance_ , _Phys. Rev. D_ 78 (2008) 115003 [0807.4668].
  * [165] S. Paßehr, _Two-Loop Corrections to the Higgs-Boson Masses in the Minimal Supersymmetric Standard Model with CP-Violation_ , Ph.D. thesis, Munich, Tech. U., 2014.
  * [166] H. Bahl, J. Braathen and G. Weiglein, _Theoretical concepts and measurement prospects for BSM trilinear couplings: a case study for scalar top quarks_ , _Eur. Phys. J. C_ 83 (2023) 685 [2212.11213].
  * [167] H. Bahl, J. Braathen and G. Weiglein, _External leg corrections as an origin of large logarithms_ , _JHEP_ 02 (2022) 159 [2112.11419].
  * [168] J. Küblbeck, M. Böhm and A. Denner, _Feyn Arts: Computer Algebraic Generation of Feynman Graphs and Amplitudes_ , _Comput. Phys. Commun._ 60 (1990) 165.
  * [169] H. Eck and J. Kublbeck, _Computeralgebraic generation of Feynman graphs and amplitudes_ , in _2nd International Workshop on Software Engineering, Artificial Intelligence and Expert Systems for High-energy and Nuclear Physics_ , 1992.
  * [170] T. Hahn, _Generating Feynman diagrams and amplitudes with FeynArts 3_ , _Comput. Phys. Commun._ 140 (2001) 418 [hep-ph/0012260].
  * [171] T. Hahn and M. Perez-Victoria, _Automatized one loop calculations in four-dimensions and D-dimensions_ , _Comput. Phys. Commun._ 118 (1999) 153 [hep-ph/9807565].
  * [172] T. Hahn, S. Paßehr and C. Schappacher, _FormCalc 9 and Extensions_ , _PoS_ LL2016 (2016) 068 [1604.04611].
  * [173] G. Weiglein, R. Scharf and M. Böhm, _Reduction of general two loop selfenergies to standard scalar integrals_ , _Nucl. Phys. B_ 416 (1994) 606 [hep-ph/9310358].
  * [174] G. Weiglein, R. Mertig, R. Scharf and M. Böhm, _Computer algebraic calculation of two loop selfenergies in the electroweak standard model_ , in _2nd International Workshop on Software Engineering, Artificial Intelligence and Expert Systems for High-energy and Nuclear Physics_ , 5, 1995\.
  * [175] M.D. Schwartz, _Quantum Field Theory and the Standard Model_ , Cambridge University Press (3, 2014).
  * [176] H.E. Haber, _The Status of the minimal supersymmetric standard model and beyond_ , _Nucl. Phys. B Proc. Suppl._ 62 (1998) 469 [hep-ph/9709450].
  * [177] Particle Data Group collaboration, _Review of Particle Physics_ , _PTEP_ 2020 (2020) 083C01.
  * [178] M. Steinhauser, _Leptonic contribution to the effective electromagnetic coupling constant up to three loops_ , _Phys. Lett. B_ 429 (1998) 158 [hep-ph/9803313].
  * [179] M. Carena, S. Heinemeyer, C.E.M. Wagner and G. Weiglein, _Suggestions for improved benchmark scenarios for Higgs boson searches at LEP-2_ , in _Workshop on New Theoretical Developments for Higgs Physics at LEP-2_ , 12, 1999 [hep-ph/9912223].
  * [180] M. Carena, S. Heinemeyer, C.E.M. Wagner and G. Weiglein, _Suggestions for Benchmark Scenarios for MSSM Higgs Boson Searches at Hadron Colliders_ , _Eur. Phys. J. C_ 26 (2003) 601 [hep-ph/0202167].
  * [181] S. Heinemeyer, W. Hollik and G. Weiglein, _FeynHiggs: A Program for the calculation of the masses of the neutral CP even Higgs bosons in the MSSM_ , _Comput. Phys. Commun._ 124 (2000) 76 [hep-ph/9812320].
  * [182] T. Hahn, S. Heinemeyer, W. Hollik, H. Rzehak and G. Weiglein, _FeynHiggs: A program for the calculation of MSSM Higgs-boson observables - Version 2.6.5_ , _Comput. Phys. Commun._ 180 (2009) 1426.
  * [183] H. Bahl, T. Hahn, S. Heinemeyer, W. Hollik, S. Paßehr, H. Rzehak et al., _Precision calculations in the MSSM Higgs-boson sector with FeynHiggs 2.14_ , _Comput. Phys. Commun._ 249 (2020) 107099 [1811.09073].
  * [184] M. Sperling, _Renormierung von Vakuumerwartungswerten in spontan gebrochenen Eichtheorien_ , diploma thesis, Dresden, Tech. U., 2013.
  * [185] J. Collins, _Foundations of perturbative QCD_ , vol. 32, Cambridge University Press (11, 2013).
  * [186] T. Hahn, “Looptools 2.15 user’s guide.” https://feynarts.de/looptools/LT215Guide.pdf, 2018.
  * [187] H.A. Rzehak, _Zwei-Schleifen-Beiträge im supersymmetrischen Higgs-Sektor_ , dissertation, Munich Tech. U., 6, 2005.
  * [188] A. Dabelstein, _Fermionic decays of neutral MSSM Higgs bosons at the one loop level_ , _Nucl. Phys. B_ 456 (1995) 25 [hep-ph/9503443].
  * [189] H.E. Logan and S.-f. Su, _Associated Production of $H^{\pm}$ and $W^{\mp}$ in High-Energy $e^{+}e^{-}$ Collisions in the Minimal Supersymmetric Standard Model_, _Phys. Rev. D_ 66 (2002) 035001 [hep-ph/0203270].
  * [190] K.E. Williams, H. Rzehak and G. Weiglein, _Higher order corrections to Higgs boson decays in the MSSM with complex parameters_ , _Eur. Phys. J. C_ 71 (2011) 1669 [1103.1335].


* [CDKS18] Clément L Canonne, Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart.  Testing conditional independence of discrete distributions.  In 2018 Information Theory and Applications Workshop (ITA), pages 1–57. IEEE, 2018.
  * [CFJL18] Emmanuel Candès, Yingying Fan, Lucas Janson, and Jinchi Lv.  Panning for gold:‘model-x’knockoffs for high dimensional controlled variable selection.  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(3):551–577, 2018.
  * [COL+05] Catarina D Campbell, Elizabeth L Ogburn, Kathryn L Lunetta, Helen N Lyon, Matthew L Freedman, Leif C Groop, David Altshuler, Kristin G Ardlie, and Joel N Hirschhorn.  Demonstrating stratification in a european american population.  Nature genetics, 37(8):868–872, 2005.
  * [CRC+13] Le Cong, F Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi Habib, Patrick D Hsu, Xuebing Wu, Wenyan Jiang, Luciano A Marraffini, et al.  Multiplex genome engineering using crispr/cas systems.  Science, 339(6121):819–823, 2013.
  * [CWZM18] Lorin Crawford, Kris C Wood, Xiang Zhou, and Sayan Mukherjee.  Bayesian approximate kernel regression with variable selection.  Journal of the American Statistical Association, 113(524):1710–1721, 2018.
  * [Daw79] A Philip Dawid.  Conditional independence in statistical theory.  Journal of the Royal Statistical Society: Series B (Methodological), 41(1):1–15, 1979.
  * [DHJ+04] Adrian Dobra, Chris Hans, Beatrix Jones, Joseph R Nevins, Guang Yao, and Mike West.  Sparse graphical models for exploring gene expression data.  Journal of Multivariate Analysis, 90(1):196–212, 2004.
  * [DHS18] Mathias Drton, Fang Han, and Hongjian Shi.  High dimensional consistent independence testing with maxima of rank correlations.  arXiv preprint arXiv:1812.06189, 2018.
  * [DS21] Nabarun Deb and Bodhisattva Sen.  Multivariate rank-based distribution-free nonparametric testing using measure transportation.  Journal of the American Statistical Association, (just-accepted):1–45, 2021.
  * [Fri04] Nir Friedman.  Inferring cellular networks using probabilistic graphical models.  Science, 303(5659):799–805, 2004.
  * [GP97] Henryk Gzyl and Jose Luis Palacios.  The weierstrass approximation theorem and large deviations.  The American mathematical monthly, 104(7):650–653, 1997.
  * [HT+96] Fushing Hsieh, Bruce W Turnbull, et al.  Nonparametric and semiparametric estimation of the receiver operating characteristic curve.  Annals of statistics, 24(1):25–40, 1996.
  * [JH13] Julie Jossea and Susan Holmesb.  Measures of dependence between random vectors and tests of independence.  arXiv preprint arXiv:1307.7383, 2013.
  * [KF09] Daphne Koller and Nir Friedman.  Probabilistic graphical models: principles and techniques.  MIT press, 2009.
  * [KR20] Eugene Katsevich and Aaditya Ramdas.  A theoretical treatment of conditional independence testing under model-x.  arXiv preprint arXiv:2005.05506, 2020.
  * [LF18] Lihua Lei and William Fithian.  Adapt: an interactive procedure for multiple testing with side information.  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(4):649–679, 2018.
  * [LGR+18] Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman.  Distribution-free predictive inference for regression.  Journal of the American Statistical Association, 113(523):1094–1111, 2018.
  * [LKJR20] Molei Liu, Eugene Katsevich, Lucas Janson, and Aaditya Ramdas.  Fast and powerful conditional randomization testing via distillation.  arXiv preprint arXiv:2006.03980, 2020.
  * [LLZ18] Faming Liang, Qizhai Li, and Lei Zhou.  Bayesian neural networks for selection of drug sensitive genes.  Journal of the American Statistical Association, 113(523):955–972, 2018.
  * [LR06] Erich L Lehmann and Joseph P Romano.  Testing statistical hypotheses.  Springer Science & Business Media, 2006.
  * [LW14] Jing Lei and Larry Wasserman.  Distribution-free prediction bands for non-parametric regression.  Journal of the Royal Statistical Society: Series B: Statistical Methodology, pages 71–96, 2014.
  * [NBW20] Matey Neykov, Sivaraman Balakrishnan, and Larry Wasserman.  Minimax optimal conditional independence testing.  arXiv preprint arXiv:2001.03039, 2020.
  * [P+00] Judea Pearl et al.  Models, reasoning and inference.  Cambridge, UK: CambridgeUniversityPress, 19, 2000.
  * [Pan08] Liam Paninski.  A coincidence-based test for uniformity given very sparsely sampled discrete data.  IEEE Transactions on Information Theory, 54(10):4750–4755, 2008\.
  * [PBSP18] Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf, and Jonas Peters.  Kernel-based tests for joint independence.  Journal of the Royal Statistical Society Series B, 80(1):5–31, 2018\.
  * [PCS+16] Jason M Peters, Alexandre Colavin, Handuo Shi, Tomasz L Czarny, Matthew H Larson, Spencer Wong, John S Hawkins, Candy HS Lu, Byoung-Mo Koo, Elizabeth Marta, et al.  A comprehensive, crispr-based functional analysis of essential genes in bacteria.  Cell, 165(6):1493–1506, 2016.
  * [PJS17] Jonas Peters, Dominik Janzing, and Bernhard Schölkopf.  Elements of causal inference: foundations and learning algorithms.  The MIT Press, 2017.
  * [RPC19] Yaniv Romano, Evan Patterson, and Emmanuel Candes.  Conformalized quantile regression.  Advances in Neural Information Processing Systems, 32:3543–3553, 2019.
  * [RRF+11] David N Reshef, Yakir A Reshef, Hilary K Finucane, Sharon R Grossman, Gilean McVean, Peter J Turnbaugh, Eric S Lander, Michael Mitzenmacher, and Pardis C Sabeti.  Detecting novel associations in large data sets.  science, 334(6062):1518–1524, 2011.
  * [SE21] Jia-Han Shih and Takeshi Emura.  On the copula correlation ratio and its generalization.  Journal of Multivariate Analysis, 182:104708, 2021.
  * [SP20] Rajen D Shah and Jonas Peters.  The hardness of conditional independence testing and the generalised covariance measure.  The Annals of Statistics, 48(3):1514–1538, 2020.
  * [SSC17] Matteo Sesia, Chiara Sabatti, and Emmanuel J Candès.  Gene hunting with knockoffs for hidden markov models.  arXiv preprint arXiv:1706.04677, 2017.
  * [Tha10] Olivier Thas.  Comparing distributions.  Springer, 2010.
  * [Tib96] Robert Tibshirani.  Regression shrinkage and selection via the lasso.  Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.
  * [TVZ+18] Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, and David M Blei.  The holdout randomization test: Principled and easy black box feature selection.  arXiv preprint arXiv:1811.00645, 1, 2018.
  * [VGS05] Vladimir Vovk, Alex Gammerman, and Glenn Shafer.  Algorithmic learning in a random world.  Springer Science & Business Media, 2005.
  * [VV17] Gregory Valiant and Paul Valiant.  An automatic inequality prover and instance optimal identity testing.  SIAM Journal on Computing, 46(1):429–455, 2017.
  * [WDM18] Luca Weihs, Mathias Drton, and Nicolai Meinshausen.  Symmetric rank covariances: a generalized framework for nonparametric measures of dependence.  Biometrika, 105(3):547–562, 2018.
  * [WJ20] Wenshuo Wang and Lucas Janson.  A power analysis of the conditional randomization test and knockoffs.  arXiv preprint arXiv:2010.02304, 2020.
  * [WS10] Xiaoquan Wen and Matthew Stephens.  Using linear predictors to impute allele frequencies from summary or pooled genotype data.  The annals of applied statistics, 4(3):1158, 2010.
  * [ZFGS18] Qinyi Zhang, Sarah Filippi, Arthur Gretton, and Dino Sejdinovic.  Large-scale kernel methods for independence testing.  Statistics and Computing, 28(1):113–130, 2018.
  * [Zha19] Kai Zhang.  Bet on independence.  Journal of the American Statistical Association, 114(528):1620–1637, 2019.
  * [ZJ20] Lu Zhang and Lucas Janson.  Floodgate: inference for model-free variable importance.  arXiv preprint arXiv:2007.01283, 2020.
  * [ZPJS12] Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf.  Kernel-based conditional independence test and application in causal discovery.  arXiv preprint arXiv:1202.3775, 2012.