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@misc{chernozhukov2017doubledebiased,
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 abstract = {The propensity score collapses the covariates of an observational study into a single measure summarizing their joint association with treatment conditions; prognostic scores summarize covariates' association with potential responses. As with propensity scores, stratification on prognostic scores brings to uncontrolled studies a concrete and desirable form of balance, a balance that is more familiar as an objective of experimental control. Like propensity scores, prognostic scores can reduce the dimension of the covariate, yet causal inferences conditional on them are as valid as are inferences conditional only on the unreduced covariate. As a method of adjustment unto itself, prognostic scoring has limitations not shared with propensity scoring, but it holds promise as a complement to the propensity score, particularly in certain designs for which unassisted propensity adjustment is difficult or infeasible.},
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  pages={1398--1407},
  year={2006},
  publisher={Taylor \& Francis}
}

@inproceedings{ugander2013graph,
  title={Graph cluster randomization: Network exposure to multiple universes},
  author={Ugander, Johan and Karrer, Brian and Backstrom, Lars and Kleinberg, Jon},
  booktitle={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={329--337},
  year={2013},
  organization={ACM}
}

@article{eckles2014design,
  title={Design and analysis of experiments in networks: Reducing bias from interference},
  author={Eckles, Dean and Karrer, Brian and Ugander, Johan},
  journal={arXiv preprint arXiv:1404.7530},
  year={2014}
}

@inproceedings{bakshy2012social,
  title={Social influence in social advertising: Evidence from field experiments},
  author={Bakshy, Eytan and Eckles, Dean and Yan, Rong and Rosenn, Itamar},
  booktitle={Proceedings of the 13th ACM Conference on Electronic Commerce},
  pages={146--161},
  year={2012},
  organization={ACM}
}

@article{eckles2016estimating,
  title={Estimating peer effects in networks with peer encouragement designs},
  author={Eckles, Dean and Kizilcec, Ren{\'e} F and Bakshy, Eytan},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={27},
  pages={7316--7322},
  year={2016},
  publisher={National Acad Sciences}
}

@article{basse2015optimal,
  title={Optimal design of experiments in the presence of network-correlated outcomes},
  author={Basse, Guillaume W and Airoldi, Edoardo M},
  journal={arXiv preprint arXiv:1507.00803},
  year={2015}
}


@book{chapin1947experimental,
  title={Experimental Designs in Sociological Research},
  author={Chapin, F.S.},
  url={https://books.google.com/books?id=DoMRngEACAAJ},
  year={1947},
  publisher={Harper; New York}
}

@book{greenwood1945experimental,
  title={Experimental sociology: A study in method},
  author={Greenwood, Ernest},
  year={1945},
  publisher={King's crown Press}
}


@article{hudgens2008toward,
  title={Toward Causal Inference With Interference},
  author={Hudgens, Michael G and Halloran, M Elizabeth},
  journal={Journal of the American Statistical Association},
  volume={103},
  number={482},
  pages={832--842},
  year={2008},
  publisher={Taylor \& Francis}
}

@article{tchetgen2012causal,
  title={On causal inference in the presence of interference},
  author={Tchetgen, Eric J Tchetgen and VanderWeele, Tyler J},
  journal={Statistical Methods in Medical Research},
  volume={21},
  number={1},
  pages={55--75},
  year={2012},
  publisher={Sage Publications}
}

@article{fan2015discoveries,
  title={Are Discoveries Spurious? Distributions of Maximum Spurious Correlations and Their Applications},
  author={Fan, Jianqing and Shao, Qi-Man and Zhou, Wen-Xin},
  journal={arXiv preprint arXiv:1502.04237},
  year={2015}
}

@article{aral2013engineering,
  title={Engineering social contagions: Optimal network seeding in the presence of homophily},
  author={Aral, Sinan and Muchnik, Lev and Sundararajan, Arun},
  journal={Network Science},
  volume={1},
  number={02},
  pages={125--153},
  year={2013},
  publisher={Cambridge Univ Press}
}

@techreport{athey2015exact,
  title={Exact p-values for network interference},
  author={Athey, Susan and Eckles, Dean and Imbens, Guido W},
  year={2015},
  institution={National Bureau of Economic Research}
}
@unpublished{AlamRu15,
author={Noor-E-Alam, M. and  Rudin, C.},
title={Robust Testing for Causal Inference in Natural Experiments},
journal={Working paper},
year={2015},
}

@unpublished{AlamRu15nonparam,
author={Noor-E-Alam, M. and  Rudin, C.},
title={Robust Nonparametric Testing for Causal Inference in Natural Experiments},
note={Working paper},
year={2015}
}
@article{aral2009distinguishing,
  title={Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks},
  author={Aral, Sinan and Muchnik, Lev and Sundararajan, Arun},
  journal={Proceedings of the National Academy of Sciences},
  volume={106},
  number={51},
  pages={21544--21549},
  year={2009},
  publisher={National Acad Sciences}
}



@article{volfovsky2016sharp,
  title={Sharp total variation bounds for finitely exchangeable arrays},
  author={Volfovsky, Alexander and Airoldi, Edoardo M},
  journal={Statistics \& Probability Letters},
  volume={114},
  pages={54--59},
  year={2016},
  publisher={North-Holland}
}

@article{sussman2015analyzing,
  title={Analyzing statistical and computational tradeoffs of estimation procedures},
  author={Sussman, Daniel L and Volfovsky, Alexander and Airoldi, Edoardo M},
  journal={arXiv preprint arXiv:1506.07925},
  year={2015}
}

@article{volfovsky2015causal,
  title={Causal inference for ordinal outcomes},
  author={Volfovsky, Alexander and Airoldi, Edoardo M and Rubin, Donald B},
  journal={arXiv preprint arXiv:1501.01234},
  year={2015}
}

@incollection{ogburn2016bigdata,
  author    = {Elizabeth L. Ogburn and
               Alexander Volfovsky},
  title     = {Statistics for networks and causal inference},
  booktitle = {Handbook of Big Data.},
  pages     = {171--190},
  year      = {2016}
}

@article{toulis2015causaleffect,
  title={Causal effects of professional networking on labor mobility},
  author={Toulis, Panos and Volfovsky, Alexander, and Airoldi, Edoardo},
  journal={unpublished manuscript},
  year={2015}
}

@article{joffe1999invited,
  title={Invited commentary: propensity scores},
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  journal={American journal of epidemiology},
  volume={150},
  number={4},
  pages={327--333},
  year={1999},
  publisher={Oxford Univ Press}
}

@article{stuart2010matching,
  title={Matching methods for causal inference: A review and a look forward},
  author={Stuart, Elizabeth A},
  journal={Statistical Science},
  volume={25},
  number={1},
  pages={1},
  year={2010},
  publisher={NIH Public Access}
}



@article{rubin2001using,
  title={Using propensity scores to help design observational studies: application to the tobacco litigation},
  author={Rubin, Donald B},
  journal={Health Services and Outcomes Research Methodology},
  volume={2},
  number={3-4},
  pages={169--188},
  year={2001},
  publisher={Springer}
}
@article{rubin2008objective,
  title={For objective causal inference, design trumps analysis},
  author={Rubin, Donald B},
  journal={The Annals of Applied Statistics},
  pages={808--840},
  year={2008}
}

@article{rubin1996matching,
  title={Matching using estimated propensity scores: relating theory to practice},
  author={Rubin, Donald B and Thomas, Neal},
  journal={Biometrics},
  pages={249--264},
  year={1996}
}

@article{ahmed2006heart,
  title={Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods},
  author={Ahmed, Ali and Husain, Ahsan and Love, Thomas E and Gambassi, Giovanni and Dell'Italia, Louis J and Francis, Gary S and Gheorghiade, Mihai and Allman, Richard M and Meleth, Sreelatha and Bourge, Robert C},
  journal={European heart journal},
  volume={27},
  number={12},
  pages={1431--1439},
  year={2006},
  publisher={Eur Soc Cardiology}
}

@article{jagadeesan2016graphcolor,
title={Design and analysis of randomized experiments in networks with interference},
author={Jagadeesan, Ravi and Pillai, Natesh, and Volfovsky, Alexander},
journal={unpublished manuscript},
year={2016}}

@article{GohRu15,
    author    = "Siong Thye Goh and Cynthia Rudin",
    title     = "Cascaded High Dimensional Histograms: An Approach to Interpretable Density Estimation for Categorical Data",
   journal="ArXiv",
     year      = "2015"
}
@article{wang2015or,
  title={Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems},
  author={Wang, Tong and Rudin, Cynthia and Doshi-Velez, Finale and Liu, Yimin and Klampfl, Erica and MacNeille, Perry},
  journal={arXiv preprint arXiv:1504.07614},
  year={2015}
}


@article{fosdick2014separable,
  title={Separable factor analysis with applications to mortality data},
  author={Fosdick, Bailey K and Hoff, Peter D},
  journal={The annals of applied statistics},
  volume={8},
  number={1},
  pages={120},
  year={2014},
  publisher={NIH Public Access}
}

@article{RudinLeMa13, 
  title={Learning Theory Analysis for Association Rules and Sequential Event Prediction},
  author={Cynthia Rudin and Benjamin Letham and David Madigan},
  journal={Journal of Machine Learning Research},
  volume={14},
  pages={3384--3436},
    year      = "2013"
}
	
	

@article{ErtekinRu11,
author=  {\c{S}eyda Ertekin and Cynthia Rudin},
title = {On Equivalence Relationships Between Classification and Ranking Algorithms},
journal={Journal of Machine Learning Research},
volume={12},
pages={2905--2929}, 
year={2011}
}

@article{ChangEtAl2012,
    author    = {Allison Chang and Cynthia Rudin and Michael Cavaretta and Robert Thomas and Gloria Chou},
    title     = "How to Reverse-Engineer Quality Rankings",
    journal = "Machine Learning",
    month={September},
    volume={88},
    issue={3},
    pages={369--398},
    year      = {2012}
}

@article{doi:10.1111/j.1468-0262.2006.00655.x,
author = {Abadie, Alberto and Imbens, Guido W.},
title = {Large Sample Properties of Matching Estimators for Average Treatment Effects},
journal = {Econometrica},
volume = {74},
number = {1},
pages = {235-267},
keywords = {Matching estimators, average treatment effects, unconfoundedness, selection on observables, potential outcomes},
doi = {10.1111/j.1468-0262.2006.00655.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0262.2006.00655.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1468-0262.2006.00655.x},
abstract = {Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N1/2-consistent in general and describe conditions under which matching estimators do attain N1/2-consistency. Second, we show that even in settings where matching estimators are N1/2-consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R.},
year = {2006}
}


@article{McCormickRuMa12, 
  title={Bayesian Hierarchical Modeling for Predicting Medical Conditions},
  author={Tyler H. McCormick and Cynthia Rudin and David Madigan},
  journal={The Annals of Applied Statistics},
  year={2012},
  volume={6},
  number={2},
  pages={652--668}
}

@article{TulabandhulaRu13,
  author  = {Theja Tulabandhula and Cynthia Rudin},
  title   = {Machine Learning with Operational Costs},
  journal = {Journal of Machine Learning Research},
  year    = {2013},
  volume  = {14},
  pages   = {1989-2028},
  url     = {http://jmlr.org/papers/v14/tulabandhula13a.html}
}

@inproceedings{TulabandhulaRuISAIM14RO,
title={Robust Optimization using Machine Learning for Uncertainty Sets},
author={Theja Tulabandhula and Cynthia Rudin},
booktitle={Proceedings of the International Symposium on Artificial Intelligence and Mathematics (ISAIM)},
year={2014}
}

@article{TulabandhulaRu14,
year={2014},
journal={Machine Learning},
title={Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge},
publisher={Springer US},
author={Tulabandhula, Theja and Rudin, Cynthia},
pages={1-34}
}

@article{TulabandhulaRu14MLJ,
  author    = {Theja Tulabandhula and Cynthia Rudin},
  title     = {On Combining Machine Learning with Decision Making},
  journal = {Machine Learning (ECML-PKDD journal track)},
  year      = {2014},
  volume     = {97},
number={1-2},
pages={33-64}
 }

@article{LethamRuMcMa15,
    author    = "Benjamin Letham and Cynthia Rudin and Tyler H. McCormick and David Madigan",
    title     = "Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model",
   journal="Annals of Applied Statistics",
     year      = "2015",
     volume = "9",
     number="3",
     pages={1350--1371}
}
	
	
@article{LethamRuMa13,
    author    = "Benjamin Letham and Cynthia Rudin and David Madigan",
    title     = "Sequential Event Prediction",
    journal={Machine Learning},
    volume = 93,
    issue={2--3},
    pages={357--380},
    year      = "2013"
}

@inproceedings{HugginsRu14, 
  title={A Statistical Learning Theory Framework for Supervised Pattern Discovery},
  author={Jonathan H. Huggins and Cynthia Rudin},
  year={2014},
  booktitle={In Proceedings of {SIAM} Conference on Data Mining {(SDM)}}
}

@ARTICLE{genmatch,
title = {Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies},
author = {Diamond, Alexis and Sekhon, Jasjeet S.},
year = {2013},
journal = {The Review of Economics and Statistics},
volume = {95},
number = {3},
pages = {932-945}
}

@article{wager2017estimation,
  title={Estimation and inference of heterogeneous treatment effects using random forests},
  author={Wager, Stefan and Athey, Susan},
  journal={Journal of the American Statistical Association},
  number={just-accepted},
  year={2017},
  publisher={Taylor \& Francis}
}

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  title={Permutation methods: a distance function approach},
  author={Mielke Jr, Paul W and Berry, Kenneth J},
  year={2007},
  publisher={Springer Science \& Business Media}
}



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  abstract={ This paper takes the results of an employment and training program thatwas run as a field experiment, in which the participants were randomlyassigned into a treatment or a control group, and compares these results to the estimates that might have been produced by an econometrician who evaluated the program using the same econometric procedures that have been used in the program evaluation literature. This comparison shows that many of these econometric procedures fail toreplicate the experimentally determined results, and suggests that researchers should be aware of the potential for specification errorsin other nonexperimental evaluations. Copyright 1986 by American Economic Association.},
  url={https://ideas.repec.org/a/aea/aecrev/v76y1986i4p604-20.html}}

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 author = {Rajeev H. Dehejia and Sadek Wahba},
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@Misc{scipy,
  author =    {Eric Jones and Travis Oliphant and Pearu Peterson and others},
  title =     {{SciPy}: Open source scientific tools for {Python}},
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@article{harder2010propensity,
  title={Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.},
  author={Harder, Valerie S and Stuart, Elizabeth A and Anthony, James C},
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  volume={15},
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}