viitteet.bib

@inproceedings{lakkaraju17,
 author = {Lakkaraju, Himabindu and Kleinberg, Jon and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil},
 title = {The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables},
 booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '17},
 year = {2017},
 isbn = {978-1-4503-4887-4},
 location = {Halifax, NS, Canada},
 pages = {275--284},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/3097983.3098066},
 doi = {10.1145/3097983.3098066},
 acmid = {3098066},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {evaluating machine learning algorithms, selective labels, unmeasured confounders, unobservables},
 language = {finnish}
}

@Article{pearl10,
   Author="Judea Pearl",
   Title="{{A}n introduction to causal inference}",
   Journal="Int J Biostat",
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836213/},
   Year="2010",
   Volume="6",
   Number="2",
   Pages="Artikkeli 7",
   Month="Helmikuu",
   language = {finnish}
}

@article{DBLP:journals/corr/abs-1807-00905,
  author    = {Maria De{-}Arteaga and
               Artur Dubrawski and
               Alexandra Chouldechova},
  title     = {Learning under selective labels in the presence of expert consistency},
  journal   = {CoRR},
  volume    = {abs/1807.00905},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.00905},
  archivePrefix = {arXiv},
  eprint    = {1807.00905},
  timestamp = {Ma, 13 Elo 2018 16:47:23 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1807-00905},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  language = {finnish}
}

@misc{zaniewski14,
    author    = "Amanda Zaniewski",
    title     = "Bail in the United States: A Review of the Literature",
    howpublished = "\url{https://www.mass.gov/files/documents/2016/09/qx/bail-in-united-states-literature-review.pdf}",
    year     = "2014",
    month    = "Marraskuu",
    note     = "PDF, haettu 12.3.2019",
  language = {finnish}
}

@article{steinberg18,
  title={Freedom Should Be Free: A Brief History of Bail Funds in the United States},
  author={Steinberg, Robin and Kalish, Lillian and Ritchin, Ezra},
  journal={UCLA Criminal Justice Law Review},
  volume={2},
  number={1},
  year={2018},
  language = {finnish}
}

@article{kalisch14,
  title={Causal structure learning and inference: a selective review},
  author={Markus Kalisch and Peter B{\"u}hlmann},
  journal={Quality Technology \& Quantitative Management},
  volume={11},
  number={1},
  pages={3--21},
  year={2014},
  publisher={Taylor \& Francis},
  language = {finnish}
}

@book{okm,
  author={{Esitutkinta- ja pakkokeinotoimikunta}},
  title={Esitutkintalain, pakkokeinolain ja poliisilain kokonaisuudistus: esitutkinta- ja pakkokeinotoimikunnan mietint{\"o}},
  pages={128--131},
  year={2009},
  publisher={Oikeusministeri{\"o}},
  address={Helsinki},
  isbn={978-952-466-824-8},
  language={finnish},
  note = {sivut 128--131}
}

@article{cnn, 
  title={California eliminates cash bail in sweeping reform}, 
  url={https://edition.cnn.com/2018/08/28/us/bail-california-bill/index.html}, 
  journal={CNN}, 
  author={Madison Park}, 
  year={2018}, 
  month={Elokuu},
  note = {viitattu 5.4.2019},
  language={finnish}
} 

@booklet{oinonen16,
  author   = "Lotta Oinonen",
  title     = "Johdatus yliopistomatematiikkaan",
  year      = "2016",
  month    = "Tammikuu",
  note     = "Johdatus yliopistomatematiikkaan -kurssin kurssimateriaali",
  language={finnish}
} 

@book{pearl18,
  title={The book of why: the new science of cause and effect},
  author={Pearl, Judea and Mackenzie, Dana},
  year={2018},
  publisher={Basic Books}
} 

@book{laaksonen13,
    author    = "Seppo Laaksonen",
    title     = "Surveymetodiikka: Aineiston kokoamisesta puhdistamisen kautta analyysiin",
    publisher = "bookboon.com",
    note  = "2. painos",
    year      = "2013",
  language={finnish}
} 

@article{propublica16, 
  title={Machine Bias}, 
  url={https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing}, 
  journal={ProPublica}, 
  author={Julia Angwin and Jeff Larson and Surya Mattu and Lauren Kirchner},
  month = "Toukokuu",
  year = "2016",
  language={finnish},
  note = {viitattu 5.4.2019}
} 

@article{madras18,
  title={Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data},
  author={Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  journal={arXiv preprint arXiv:1809.02519},
  year={2018},
  language={finnish}
} 

@booklet{kivinen18,
  author   = "Jyrki Kivinen",
  title     = "Tietorakenteet ja algoritmit",
  year      = "2018",
  month    = "Kevät",
  note     = "Tietorakenteet ja algoritmit -kurssin kurssimateriaali",
  language={finnish}
} 

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011},
 language={finnish}
}

@article{willmott05,
  title={Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance},
  author={Willmott, Cort J and Matsuura, Kenji},
  journal={Climate research},
  volume={30},
  number={1},
  pages={79--82},
  year={2005},
  language={finnish}
}

@article{stan,
   author = {Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell},
   title = {Stan: A Probabilistic Programming Language},
   journal = {Journal of Statistical Software, Articles},
   volume = {76},
   number = {1},
   year = {2017},
   keywords = {probabilistic programming; Bayesian inference; algorithmic differentiation; Stan},
   abstract = {Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.},
   issn = {1548-7660},
   pages = {1--32},
   doi = {10.18637/jss.v076.i01},
   url = {https://www.jstatsoft.org/v076/i01},
   language={finnish}
}

@manual{compas, 
  title={Practitioner’s Guide to COMPAS Core}, 
  url={https://assets.documentcloud.org/documents/2840784/Practitioner-s-Guide-to-COMPAS-Core.pdf}, 
  author	= {{Northpointe}},
  year={2015}, 
  month={maaliskuu},
  note = {Viitattu 4.10.2019},
  language={finnish}
} 

@mastersthesis{sanz19,
  title = {Kertymä-logit-regressioanalyysi lapsen tapaamisoikeuden täytäntöönpanopäätöksistä},
  author = {Sanz, Aune},
  school = {Helsingin yliopisto},
  year = {2019},
  url = {http://hdl.handle.net/10138/302857},
  url = {http://www.urn.fi/URN:NBN:fi:hulib-201906132857},
  type = {Pro gradu -tutkielma},
  note = {Viitattu 7.10.2019},
  language = {finnish}
}

@misc{statevloomis,
    author    = "{Wisconsinin korkein oikeus}",
    title     = "State v. Loomis",
    year     = "2016",
    month    = "kes{\"a}kuu",
    url = {https://law.justia.com/cases/wisconsin/supreme-court/2016/2015ap000157-cr.html},
    note     = "Viitattu 7.10.2019",
    language = {finnish}
}

@booklet{hyvonen17,
  author   = "Ville Hyv{\"o}nen",
  title     = "Bayesian Inference 2017",
  year      = "2017",
  month    = "joulukuu",
  note     = "Bayesian Inference -kurssin kurssimateriaali",
  language={finnish}
} 

@mastersthesis{tikka15,
    author    = "Santtu Tikka",
    title     = "Kausaalivaikutusten identifiointi algoritmisesti",
    school    = "{Jyväskyl{\"a}n yliopisto}",
    type     = "Pro gradu -tutkielma",
    year      = "2015",
    month    = "helmikuu",
    note     = "Viitattu 7.10.2019",
    language = {finnish}
}

@phdthesis{tikka18,
    author    = "Santtu Tikka",
    title     = "Improving identification algorithms in causal inference",
    school    = "{Jyväskyl{\"a}n yliopisto}",
    type     = "Väitöskirja",
    year      = "2018",
    month    = "elokuu",
    note     = "Viitattu 9.10.2019",
    language = {finnish}
}

@book{vehkalahti19,
  title = {Multivariate analysis for the behavioral sciences},
  author = {Vehkalahti, Kimmo and Everitt, Brian},
  address = {Boca Raton, Florida},
  publisher = {CRC Press},
  year = {2019},
  pages = {415},
  note = {Ensimmäisen osan kirjoittaja: Brian S. Everitt. 2. painos.},
  note = {Earlier edition published as: Multivariable modeling and multivariate analysis for the behavioral sciences / [by] Brian S. Everitt.},
  url = {http://login.libproxy.helsinki.fi/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1987994}
}

@article{brennan09,
  title={Evaluating the predictive validity of the COMPAS risk and needs assessment system},
  author={Brennan, Tim and Dieterich, William and Ehret, Beate},
  journal={Criminal Justice and Behavior},
  volume={36},
  number={1},
  pages={21--40},
  year={2009},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@article{dieterich16,
  title={COMPAS risk scales: Demonstrating accuracy equity and predictive parity},
  author={Dieterich, William and Mendoza, Christina and Brennan, Tim},
  journal={Northpoint Inc},
  year={2016}
}

@article{fass08,
  title={The LSI-R and the COMPAS: Validation data on two risk-needs tools},
  author={Fass, Tracy L and Heilbrun, Kirk and DeMatteo, David and Fretz, Ralph},
  journal={Criminal Justice and Behavior},
  volume={35},
  number={9},
  pages={1095--1108},
  year={2008},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}