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      primaryClass={cs.CL}
}

@misc{deshpande2023calibrated,
      title={Calibrated Uncertainty Estimation Improves Bayesian Optimization}, 
      author={Shachi Deshpande and Volodymyr Kuleshov},
      year={2023},
      eprint={2112.04620},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{kuleshov2018accurate,
      title={Accurate Uncertainties for Deep Learning Using Calibrated Regression}, 
      author={Volodymyr Kuleshov and Nathan Fenner and Stefano Ermon},
      year={2018},
      eprint={1807.00263},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{vovk2005defensive,
  author    = {Vladimir Vovk and
               Akimichi Takemura and
               Glenn Shafer},
  title     = {Defensive Forecasting},
  booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence
               and Statistics, {AISTATS} 2005, Bridgetown, Barbados, January 6-8,
               2005},
  year      = {2005},
  url       = {http://www.gatsby.ucl.ac.uk/aistats/fullpapers/224.pdf},
  timestamp = {Wed, 06 May 2015 20:37:32 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/aistats/VovkTS05},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@misc{tibshirani2019conformal,
  doi = {10.48550/ARXIV.1904.06019},
  
  url = {https://arxiv.org/abs/1904.06019},
  
  author = {Tibshirani, Ryan J. and Barber, Rina Foygel and Candes, Emmanuel J. and Ramdas, Aaditya},
  
  keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Conformal Prediction Under Covariate Shift},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@unknown{Barber2022Conformal,
author = {Barber, Rina and Candes, Emmanuel and Ramdas, Aaditya and Tibshirani, Ryan},
year = {2022},
month = {02},
pages = {},
title = {Conformal prediction beyond exchangeability}
}

@ARTICLE{Devlin1999-mr,
  title     = "Genomic control for association studies",
  author    = "Devlin, B and Roeder, K",
  journal   = "Biometrics",
  publisher = "Wiley",
  volume    =  55,
  number    =  4,
  pages     = "997--1004",
  month     =  dec,
  year      =  1999,
  language  = "en"
}

@ARTICLE{Epstein2007-tz,
  title     = "A simple and improved correction for population stratification
               in case-control studies",
  author    = "Epstein, Michael P and Allen, Andrew S and Satten, Glen A",
  journal   = "Am. J. Hum. Genet.",
  publisher = "Elsevier BV",
  volume    =  80,
  number    =  5,
  pages     = "921--930",
  month     =  may,
  year      =  2007,
  copyright = "https://www.elsevier.com/open-access/userlicense/1.0/",
  language  = "en"
}

@article {Imbens2004propensity,
	title = {The Propensity Score with Continuous Treatments},
	year = {2004},
	author = {Guido Imbens and Keisuke Hirano}
}

@article{gneiting2007strictly,
author = {Tilmann Gneiting and Adrian E Raftery},
title = {Strictly Proper Scoring Rules, Prediction, and Estimation},
journal = {Journal of the American Statistical Association},
volume = {102},
number = {477},
pages = {359-378},
year  = {2007},
publisher = {Taylor & Francis},
doi = {10.1198/016214506000001437},

URL = { 
        https://doi.org/10.1198/016214506000001437
    
},
eprint = { 
        https://doi.org/10.1198/016214506000001437
    
}

}

@article{gneiting2007probabilistic,
  author = {T. Gneiting and F. Balabdaoui and A. E. Raftery},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  number = {2},
  pages = {243--268},
  title = {Probabilistic forecasts, calibration and sharpness},
  volume = {69},
  year = {2007},
}

@misc{angelopoulos2021gentle,
  doi = {10.48550/ARXIV.2107.07511},
  
  url = {https://arxiv.org/abs/2107.07511},
  
  author = {Angelopoulos, Anastasios N. and Bates, Stephen},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Statistics Theory (math.ST), Methodology (stat.ME), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
  
  title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@ARTICLE{Rosenbaum1983-rp,
  title     = "The central role of the propensity score in observational
               studies for causal effects",
  author    = "Rosenbaum, Paul R and Rubin, Donald B",
  journal   = "Biometrika",
  publisher = "JSTOR",
  volume    =  70,
  number    =  1,
  pages     = "41",
  month     =  apr,
  year      =  1983
}

@ARTICLE{Robins2000-kz,
  title     = "On Profile Likelihood: Comment",
  author    = "Robins, James M and Rotnitzky, Andrea and van der Laan, Mark",
  journal   = "J. Am. Stat. Assoc.",
  publisher = "JSTOR",
  volume    =  95,
  number    =  450,
  pages     = "477",
  month     =  jun,
  year      =  2000
}


@ARTICLE{McCaffrey2004-cr,
  title     = "Propensity score estimation with boosted regression for
               evaluating causal effects in observational studies",
  author    = "McCaffrey, Daniel F and Ridgeway, Greg and Morral, Andrew R",
  journal   = "Psychol. Methods",
  publisher = "American Psychological Association (APA)",
  volume    =  9,
  number    =  4,
  pages     = "403--425",
  month     =  dec,
  year      =  2004,
  language  = "en"
}

@ARTICLE{Imbens2004-uy,
  title     = "Nonparametric estimation of average treatment effects under
               exogeneity: A review",
  author    = "Imbens, Guido W",
  journal   = "Rev. Econ. Stat.",
  publisher = "MIT Press - Journals",
  volume    =  86,
  number    =  1,
  pages     = "4--29",
  month     =  feb,
  year      =  2004,
  language  = "en"
}

@misc{smith2020tutorial,
      title={Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code}, 
      author={Matthew J. Smith and Camille Maringe and Bernard Rachet and Mohammad A. Mansournia and Paul N. Zivich and Stephen R. Cole and Miguel Angel Luque-Fernandez},
      year={2020},
      eprint={2012.09920},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}

@misc{shafer2007tutorial,
      title={A tutorial on conformal prediction}, 
      author={Glenn Shafer and Vladimir Vovk},
      year={2007},
      eprint={0706.3188},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{pearl2000models,
  title={Models, reasoning and inference},
  author={Pearl, Judea and others},
  journal={Cambridge, UK: CambridgeUniversityPress},
  volume={19},
  year={2000}
}
@inproceedings{kallus2020deepmatch,
  title={Deepmatch: Balancing deep covariate representations for causal inference using adversarial training},
  author={Kallus, Nathan},
  booktitle={International Conference on Machine Learning},
  pages={5067--5077},
  year={2020},
  organization={PMLR}
}

@misc{veitch2019using,
      title={Using Embeddings to Correct for Unobserved Confounding in Networks}, 
      author={Victor Veitch and Yixin Wang and David M. Blei},
      year={2019},
      eprint={1902.04114},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{veitch2020adapting,
      title={Adapting Text Embeddings for Causal Inference}, 
      author={Victor Veitch and Dhanya Sridhar and David M. Blei},
      year={2020},
      eprint={1905.12741},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@inproceedings{pryzant2017predicting,
  title={Predicting sales from the language of product descriptions},
  author={Pryzant, Reid and Chung, Youngjoo and Jurafsky, Dan},
  booktitle={eCOM@ SIGIR},
  year={2017}
}

@article{hirano2003efficient,
title = "Efficient estimation of average treatment effects using the estimated propensity score",
abstract = "We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates. Although adjusting for differences in the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, and Todd (1998), and Robins, Mark, and Newey (1992). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to an efficient estimate of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.",
author = "Keisuke Hirano and Imbens, {Guido W.} and Geert Ridder",
year = "2003",
doi = "10.1111/1468-0262.00442",
language = "English (US)",
volume = "71",
pages = "1161--1189",
journal = "Econometrica",
issn = "0012-9682",
publisher = "Wiley-Blackwell",
number = "4",
}

@ARTICLE{Lee2010-eu,
  title     = "Improving propensity score weighting using machine learning",
  author    = "Lee, Brian K and Lessler, Justin and Stuart, Elizabeth A",
  abstract  = "Machine learning techniques such as classification and
               regression trees (CART) have been suggested as promising
               alternatives to logistic regression for the estimation of
               propensity scores. The authors examined the performance of
               various CART-based propensity score models using simulated data.
               Hypothetical studies of varying sample sizes (n=500, 1000, 2000)
               with a binary exposure, continuous outcome, and 10 covariates
               were simulated under seven scenarios differing by degree of
               non-linear and non-additive associations between covariates and
               the exposure. Propensity score weights were estimated using
               logistic regression (all main effects), CART, pruned CART, and
               the ensemble methods of bagged CART, random forests, and boosted
               CART. Performance metrics included covariate balance, standard
               error, per cent absolute bias, and 95 per cent confidence
               interval (CI) coverage. All methods displayed generally
               acceptable performance under conditions of either non-linearity
               or non-additivity alone. However, under conditions of both
               moderate non-additivity and moderate non-linearity, logistic
               regression had subpar performance, whereas ensemble methods
               provided substantially better bias reduction and more consistent
               95 per cent CI coverage. The results suggest that ensemble
               methods, especially boosted CART, may be useful for propensity
               score weighting.",
  journal   = "Stat. Med.",
  publisher = "Wiley",
  volume    =  29,
  number    =  3,
  pages     = "337--346",
  month     =  feb,
  year      =  2010,
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@ARTICLE{DAgostino1998-ex,
  title     = "Propensity score methods for bias reduction in the comparison of
               a treatment to a non-randomized control group",
  author    = "D'Agostino, Ralph B",
  journal   = "Stat. Med.",
  publisher = "Wiley",
  volume    =  17,
  number    =  19,
  pages     = "2265--2281",
  month     =  oct,
  year      =  1998,
  language  = "en"
}
@ARTICLE{VanderWeele2006-yf,
  title     = "The use of propensity score methods in psychiatric research",
  author    = "VanderWeele, Tyler",
  journal   = "Int. J. Methods Psychiatr. Res.",
  publisher = "Wiley",
  volume    =  15,
  number    =  2,
  pages     = "95--103",
  month     =  jun,
  year      =  2006,
  language  = "en"
}

@misc{tan2017regularized,
      title={Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data}, 
      author={Zhiqiang Tan},
      year={2017},
      eprint={1710.08074},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}

@article{greenland1999confounding,
author = {Sander Greenland and Judea Pearl and James M. Robins},
title = {{Confounding and Collapsibility in Causal Inference}},
volume = {14},
journal = {Statistical Science},
number = {1},
publisher = {Institute of Mathematical Statistics},
pages = {29 -- 46},
keywords = {Bias, , , , , , , , , , ., causation, Collapsibility, confounding, Contingency tables, exchangeability, observational studies, odds ratio, relative risk, risk assessment, Simpson's paradox},
year = {1999},
doi = {10.1214/ss/1009211805},
URL = {https://doi.org/10.1214/ss/1009211805}
}

@InProceedings{kullflach2015novel,
author="Kull, Meelis
and Flach, Peter",
editor="Appice, Annalisa
and Rodrigues, Pedro Pereira
and Santos Costa, V{\'i}tor
and Soares, Carlos
and Gama, Jo{\~a}o
and Jorge, Al{\'i}pio",
title="Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration",
booktitle="Machine Learning and Knowledge Discovery in Databases",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="68--85",
isbn="978-3-319-23528-8"
}



@ARTICLE{Lanza2013-ul,
  title     = "Drawing causal inferences using propensity scores: a practical
               guide for community psychologists",
  author    = "Lanza, Stephanie T and Moore, Julia E and Butera, Nicole M",
  journal   = "Am. J. Community Psychol.",
  publisher = "Wiley",
  volume    =  52,
  number    = "3-4",
  pages     = "380--392",
  month     =  dec,
  year      =  2013,
  language  = "en"
}








