references.bib

@article{neyman1923applications,
  title={Sur les applications de la th{\'e}orie des probabilit{\'e}s aux experiences agricoles: Essai des principes},
  author={Neyman, Jersey},
  journal={Roczniki Nauk Rolniczych},
  volume={10},
  number={1},
  pages={1--51},
  year={1923}
}

@article{ding2018causal,
  title={Causal inference},
  author={Ding, Peng and Li, Fan},
  journal={Statistical Science},
  volume={33},
  number={2},
  pages={214--237},
  year={2018},
  publisher={JSTOR},
  url={https://projecteuclid.org/journals/statistical-science/volume-33/issue-2/Causal-Inference-A-Missing-Data-Perspective/10.1214/18-STS645.pdf}
}

@article{rubin1974estimating,
  title={Estimating causal effects of treatments in randomized and nonrandomized studies.},
  author={Rubin, Donald B},
  journal={Journal of educational Psychology},
  volume={66},
  number={5},
  pages={688},
  year={1974},
  publisher={American Psychological Association},
  url={http://www.fsb.muohio.edu/lij14/420_paper_Rubin74.pdf}
}

@article{rubin1978bayesian,
  title={Bayesian inference for causal effects: The role of randomization},
  author={Rubin, Donald B},
  journal={The Annals of statistics},
  pages={34--58},
  year={1978},
  publisher={JSTOR},
  url={https://www.jstor.org/stable/2958688}
}

@article{manski2020lure,
  title={The lure of incredible certitude},
  author={Manski, Charles F},
  journal={Economics \& Philosophy},
  volume={36},
  number={2},
  pages={216--245},
  year={2020},
  publisher={Cambridge University Press},
  url={https://www.nber.org/system/files/working_papers/w24905/w24905.pdf}
}

@misc{wasserstein2016asa,
  title={The ASA statement on p-values: context, process, and purpose},
  author={Wasserstein, Ronald L and Lazar, Nicole A},
  journal={The American Statistician},
  volume={70},
  number={2},
  pages={129--133},
  year={2016},
  publisher={Taylor \& Francis},
  url={https://www.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108}
}

@article{finucane2018works,
  title={What works for whom? A Bayesian approach to channeling big data streams for public program evaluation},
  author={Finucane, Mariel McKenzie and Martinez, Ignacio and Cody, Scott},
  journal={American Journal of Evaluation},
  volume={39},
  number={1},
  pages={109--122},
  year={2018},
  publisher={SAGE Publications Sage CA: Los Angeles, CA},
  url={https://journals.sagepub.com/doi/abs/10.1177/1098214017737173}
}

@article{kassler2018beyond,
  title={Beyond “Treatment versus Control”: How Bayesian Analysis Makes Factorial Experiments Feasible in Education Research},
  author={Kassler, Daniel and Nichols-Barrer, Ira and Finucane, Mariel},
  year={2018},
  url={https://journals.sagepub.com/doi/abs/10.1177/0193841X18818903},
  doi={10.1177/0193841X18818903}
}

@book{thaler2021nudge,
  title={Nudge: The final edition},
  author={Thaler, Richard H and Sunstein, Cass R},
  year={2021},
  publisher={Yale University Press}
}

@book{thaler2009nudge,
  title={Nudge: Improving decisions about health, wealth, and happiness},
  author={Thaler, Richard H and Sunstein, Cass R},
  year={2009},
  publisher={Penguin}
}

@article{chandler2020speaking,
  title={Speaking on data’s behalf: What researchers say and how audiences choose},
  author={Chandler, Jesse J and Martinez, Ignacio and Finucane, Mariel M and Terziev, Jeffrey G and Resch, Alexandra M},
  journal={Evaluation Review},
  volume={44},
  number={4},
  pages={325--353},
  year={2020},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

@article{gigerenzer2004null,
  title={The null ritual},
  author={Gigerenzer, Gerd and Krauss, Stefan and Vitouch, Oliver},
  journal={The Sage handbook of quantitative methodology for the social sciences},
  pages={391--408},
  year={2004},
  publisher={Sage Thousand Oaks, CA}
}

@article{hoekstra2014robust,
  title={Robust misinterpretation of confidence intervals},
  author={Hoekstra, Rink and Morey, Richard D and Rouder, Jeffrey N and Wagenmakers, Eric-Jan},
  journal={Psychonomic bulletin \& review},
  volume={21},
  pages={1157--1164},
  year={2014},
  publisher={Springer},
  url={https://ejwagenmakers.com/inpress/HoekstraEtAlPBR.pdf},
  doi={10.3758/s13423-013-0572-3}
}

@article{chernozhukov2024applied,
  title={Applied causal inference powered by ML and AI},
  author={Chernozhukov, Victor and Hansen, Christian and Kallus, Nathan and Spindler, Martin and Syrgkanis, Vasilis},
  volume={12},
  number={1},
  pages={338},
  year={2024},
  url={https://causalml-book.org/assets/chapters/CausalML_chap_2.pdf}
}


@techreport{wwc_baseline,
title={What Works Clearinghouse Baseline Equivalence Standard},
author={WWC},
year={2020},
institution={U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance},
url={https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/WWC-Baseline-Brief-v6_508.pdf}
}

@book{rce,
    author = {Bagby, Emilie and Rangarajan, Anu},
    isbn = {9780190059668},
    title = "{Using Rapid-Cycle Evaluation to Improve Program Design and Delivery}",
    booktitle = "{The Oxford Handbook of Program Design and Implementation Evaluation}",
    publisher = {Oxford University Press},
    year = {2023},
    month = {12},
    abstract = "{This chapter discusses rapid-cycle evaluation (RCE) approaches to inform decisions related to program design and to support ongoing program improvement. As the name suggests, RCEs involve quick systematic tests to facilitate learning around programmatic elements. They can be used to diagnose challenges, identify facilitators to implementation and take-up, and test potential solutions. RCEs can be useful at early stages of implementation or program design to help identify operational choices that can maximize chances of a program’s success, as well as during program implementation if a program does not appear to be achieving the desired results. RCE approaches can also be used to test short-term program effectiveness, when taking a program to scale, or when seeking to adapt a successful implementation effort in a different context. This chapter describes the steps involved in implementing RCE and provides several instances of using RCE for program design and improvement.}",
    doi = {10.1093/oxfordhb/9780190059668.013.7},
    url = {https://doi.org/10.1093/oxfordhb/9780190059668.013.7},
    eprint = {https://academic.oup.com/book/0/chapter/417442704/chapter-ag-pdf/56916905/book\_49435\_section\_417442704.ag.pdf},
}

@article{li2023bayesian,
  title={Bayesian causal inference: a critical review},
  author={Li, Fan and Ding, Peng and Mealli, Fabrizia},
  journal={Philosophical Transactions of the Royal Society A},
  volume={381},
  number={2247},
  pages={20220153},
  year={2023},
  publisher={The Royal Society},
  doi={10.1098/rsta.2022.0153}
}

@article{wainer2007most,
  title={The most dangerous equation},
  author={Wainer, Howard},
  journal={American Scientist},
  volume={95},
  number={3},
  pages={249},
  year={2007},
  urp={https://www.americanscientist.org/article/the-most-dangerous-equation}
}

@article{brodersen2015inferring,
  title={Inferring causal impact using Bayesian structural time-series models},
  author={Brodersen, Kay H and Gallusser, Fabian and Koehler, Jim and Remy, Nicolas and Scott, Steven L},
  year={2015},
  doi={10.1214/14-AOAS788},
  journal={Annals of Applied Statistics},
  pages={247--274},
  volume	= {9}
}

@article{zhang2023evaluating,
  title={Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix},
  author={Zhang, Vickie and Zhao, Michael and Le, Anh and Kallus, Nathan},
  journal={arXiv preprint arXiv:2311.11922},
  year={2023}
}

@article{imbens2022long,
  title={Long-term causal inference under persistent confounding via data combination},
  author={Imbens, Guido and Kallus, Nathan and Mao, Xiaojie and Wang, Yuhao},
  journal={arXiv preprint arXiv:2202.07234},
  year={2022}
}

@techreport{athey2019surrogate,
  title={The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely},
  author={Athey, Susan and Chetty, Raj and Imbens, Guido W and Kang, Hyunseung},
  year={2019},
  institution={National Bureau of Economic Research}
}

@article{chen2023semiparametric,
  title={Semiparametric estimation of long-term treatment effects},
  author={Chen, Jiafeng and Ritzwoller, David M},
  journal={Journal of Econometrics},
  volume={237},
  number={2},
  pages={105545},
  year={2023},
  publisher={Elsevier}
}

@misc{martinez2023bayesian,
      title={Bayesian and Frequentist Inference for Synthetic Controls}, 
      author={Ignacio Martinez and Jaume Vives-i-Bastida},
      year={2023},
      eprint={2206.01779},
      archivePrefix={arXiv},
      primaryClass={id='stat.ME' full_name='Methodology' is_active=True alt_name=None in_archive='stat' is_general=False description='Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods'}
}

@misc{abadie2022synthetic,
      title={Synthetic Controls in Action}, 
      author={Alberto Abadie and Jaume Vives-i-Bastida},
      year={2022},
      eprint={2203.06279},
      archivePrefix={arXiv},
      primaryClass={id='stat.ME' full_name='Methodology' is_active=True alt_name=None in_archive='stat' is_general=False description='Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods'}
}


@article{abadie2015comparative,
  title={Comparative politics and the synthetic control method},
  author={Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
  journal={American Journal of Political Science},
  volume={59},
  number={2},
  pages={495--510},
  year={2015},
  publisher={Wiley Online Library}
}

@incollection{gelman2013bayesian,
title={Parallel Experiments in Eight Schools},
author={Gelman, A. and Carlin, J.B. and Stern, H.S. and Dunson, D.B. and Vehtari, A. and Rubin, D.B.},
booktitle={Bayesian Data Analysis, Third Edition},
chapter={5},
section={5},
pages={119},
year={2013},
publisher={Taylor \& Francis},
address={New York},
series={Chapman \& Hall/CRC Texts in Statistical Science},
isbn={9781439840955},
lccn={2013039507},
url={https://books.google.com/books?id=ZXL6AQAAQBAJ}
}

@article{holland1986statistics,
  title={Statistics and causal inference},
  author={Holland, Paul W},
  journal={Journal of the American statistical Association},
  volume={81},
  number={396},
  pages={945--960},
  year={1986},
  publisher={Taylor \& Francis},
  doi={10.2307/2289064}
}

@Misc{rstanarm,
  title = {rstanarm: {Bayesian} applied regression modeling via {Stan}.},
  author = {Ben Goodrich and Jonah Gabry and Imad Ali and Sam Brilleman},
  note = {R package version 2.21.1},
  year = {2020},
  url = {https://mc-stan.org/rstanarm},
}

@Article{brms,
  title = {{brms}: An {R} Package for {Bayesian} Multilevel Models Using {Stan}},
  author = {Paul-Christian Bürkner},
  journal = {Journal of Statistical Software},
  year = {2017},
  volume = {80},
  number = {1},
  pages = {1--28},
  doi = {10.18637/jss.v080.i01},
  encoding = {UTF-8},
}

@article{JSSv076i01,
 title={Stan: A Probabilistic Programming Language},
 volume={76},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v076i01},
 doi={10.18637/jss.v076.i01},
 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.},
 number={1},
 journal={Journal of Statistical Software},
 author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
 year={2017},
 pages={1–32}
}

@article{athey2017state,
  title={The state of applied econometrics: Causality and policy evaluation},
  author={Athey, Susan and Imbens, Guido W},
  journal={Journal of Economic perspectives},
  volume={31},
  number={2},
  pages={3--32},
  year={2017},
  publisher={American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203-2418}
}

@article{abadie2021using,
  title={Using synthetic controls: Feasibility, data requirements, and methodological aspects},
  author={Abadie, Alberto},
  journal={Journal of Economic Literature},
  volume={59},
  number={2},
  pages={391--425},
  year={2021},
  publisher={American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203-2425}
}

@inbook{cunningham2021potential,
  author    = {Cunningham, Scott},
  title     = {Potential Outcomes Causal Model},
  booktitle = {Causal Inference: The Mixtape},
  chapter   = {4},
  year      = {2021},
  publisher = {Yale University Press},
  url       = {https://mixtape.scunning.com/04-potential_outcomes}
}

@article{chipman2010bart,
  author = {Hugh A. Chipman and Edward I. George and Robert E. McCulloch},
  title = {{BART: Bayesian additive regression trees}},
  volume = {4},
  journal = {The Annals of Applied Statistics},
  number = {1},
  publisher = {Institute of Mathematical Statistics},
  pages = {266 -- 298},
  keywords = {Bayesian backfitting, boosting, CART, ‎classification‎, ensemble, MCMC, Nonparametric regression, probit model, random basis, regularizatio, sum-of-trees model, Variable selection, weak learner},
  year = {2010},
  doi = {10.1214/09-AOAS285},
  URL = {https://doi.org/10.1214/09-AOAS285}
}

@article{hill2011bayesian,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  number={1},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis},
  doi = {10.1198/jcgs.2010.08162}
}

@article{thal2023causal,
  title={Causal Methods Madness: Lessons Learned from the 2022 ACIC Competition to Estimate Health Policy Impacts},
  author={Thal, Dan RC and Finucane, Mariel M},
  journal={Observational Studies},
  volume={9},
  number={3},
  pages={3--27},
  year={2023},
  publisher={University of Pennsylvania Press},
  doi = {110.1353/obs.2023.0023},
}

@article{hahn2020bayesian,
  title={Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion)},
  author={Hahn, P Richard and Murray, Jared S and Carvalho, Carlos M},
  journal={Bayesian Analysis},
  volume={15},
  number={3},
  pages={965--1056},
  year={2020},
  publisher={International Society for Bayesian Analysis},
  doi = {10.1214/19-BA1195}
}

@article{wang2024longbet,
  title={LongBet: Heterogeneous Treatment Effect Estimation in Panel Data},
  author={Wang, Meijia and Martinez, Ignacio and Hahn, P Richard},
  journal={arXiv preprint arXiv:2406.02530},
  year={2024},
  doi = {arXiv:2406.02530}
}

@Manual{stochastictree,
  title = {stochtree: Stochastic tree ensembles (XBART and BART) for supervised learning and causal inference},
  author = {Drew Herren and Richard Hahn and Jared Murray and Carlos Carvalho and Jingyu He},
  year = {2024},
  note = {R package version 0.0.0.9000},
  url = {https://stochastictree.github.io/stochtree-r/},
}


@InProceedings{krantsevich23a,
  title = 	 {Stochastic Tree Ensembles for Estimating Heterogeneous Effects},
  author =       {Krantsevich, Nikolay and He, Jingyu and Hahn, P. Richard},
  booktitle = 	 {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {6120--6131},
  year = 	 {2023},
  editor = 	 {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
  volume = 	 {206},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v206/krantsevich23a/krantsevich23a.pdf},
  url = 	 {https://proceedings.mlr.press/v206/krantsevich23a.html},
  abstract = 	 {Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a recent method that has been documented to perform well on data generating processes with strong confounding of the sort that is plausible in many applications. This paper develops a novel algorithm for fitting the BCF model, which is more efficient than the previous Gibbs sampler. The new algorithm can be used to initialize independent chains of the existing Gibbs sampler leading to better posterior exploration and coverage of the associated interval estimates in simulation studies. The new algorithm is compared to related approaches via simulation studies as well as an empirical analysis.}
}

@inproceedings{he2019xbart,
  title={XBART: Accelerated Bayesian additive regression trees},
  author={He, Jingyu and Yalov, Saar and Hahn, P Richard},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1130--1138},
  year={2019},
  organization={PMLR},
  url = 	 {https://proceedings.mlr.press/v89/he19a.html}
}

@book{duke2019thinking,
  title={Thinking in bets: Making smarter decisions when you don't have all the facts},
  author={Duke, Annie},
  year={2019},
  publisher={Penguin},
  url = 	 {https://www.google.com/books/edition/Thinking_in_Bets/CI-RDwAAQBAJ}
}

@Article{stuart2011matchit,
  title = {{MatchIt}: Nonparametric Preprocessing for Parametric Causal Inference},
  author = {Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart},
  year = {2011},
  journal = {Journal of Statistical Software},
  volume = {42},
  number = {8},
  pages = {1--28},
  doi = {10.18637/jss.v042.i08},
}

@article{ho2007matching,
  title={Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference},
  author={Ho, Daniel E and Imai, Kosuke and King, Gary and Stuart, Elizabeth A},
  journal={Political analysis},
  volume={15},
  number={3},
  pages={199--236},
  year={2007},
  publisher={Cambridge University Press}
}

@article{King_Nielsen_2019,
  title={Why Propensity Scores Should Not Be Used for Matching},
  volume={27},
  DOI={10.1017/pan.2019.11},
  number={4},
  journal={Political Analysis},
  author={King, Gary and Nielsen, Richard},
  year={2019},
  pages={435–454}
  } 
  
@article{rubin1984bayesianly,
  author = {Donald B. Rubin},
  title = {{Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician}},
  volume = {12},
  journal = {The Annals of Statistics},
  number = {4},
  publisher = {Institute of Mathematical Statistics},
  pages = {1151 -- 1172},
  keywords = {62-07, Calibration, Empirical Bayes, inference, model monitoring, operating characteristics, posterior predictive checks, Stopping rules},
  year = {1984},
  doi = {10.1214/aos/1176346785},
  URL = {https://doi.org/10.1214/aos/1176346785}
} 

@article{kruschke2018bayesian,
  title={Bayesian data analysis for newcomers},
  author={Kruschke, John K and Liddell, Torrin M},
  journal={Psychonomic bulletin \& review},
  volume={25},
  number={1},
  pages={155--177},
  year={2018},
  doi = {10.3758/s13423-017-1272-1},
  publisher={Springer}
}

@book{gelman2013bayesian,
  title={Bayesian Data Analysis, Third Edition},
  author={Gelman, A. and Carlin, J.B. and Stern, H.S. and Dunson, D.B. and Vehtari, A. and Rubin, D.B.},
  isbn={9781439840955},
  lccn={2013039507},
  series={Chapman \& Hall/CRC Texts in Statistical Science},
  url={http://www.stat.columbia.edu/~gelman/book/},
  year={2013},
  publisher={Taylor \& Francis}
}

@book{mcelreath2018statistical,
  title={Statistical Rethinking: A Bayesian Course with Examples in R and Stan},
  author={McElreath, R.},
  isbn={9781315362618},
  lccn={2015032008},
  series={Chapman \& Hall/CRC Texts in Statistical Science},
  url={https://books.google.com/books?id=T3FQDwAAQBAJ},
  year={2018},
  publisher={CRC Press}
}

@book{mcelreath2018statistical14,
title={Statistical Rethinking: A Bayesian Course with Examples in R and Stan},
author={McElreath, R.},
isbn={9781315362618},
lccn={2015032008},
series={Chapman & Hall/CRC Texts in Statistical Science},
url={https://books.google.com/books?id=T3FQDwAAQBAJ},
year={2018},
publisher={CRC Press},
chapter={14}
}

@Manual{shinylive,
  title = {shinylive: Run 'shiny' Applications in the Browser},
  author = {Barret Schloerke and Winston Chang and George Stagg and Garrick Aden-Buie},
  year = {2024},
  note = {R package version 0.2.0,
    https://github.com/posit-dev/r-shinylive},
  url = {https://posit-dev.github.io/r-shinylive/},
}

@article{hedges1981distribution,
  title={Distribution theory for Glass's estimator of effect size and related estimators},
  author={Hedges, Larry V},
  journal={journal of Educational Statistics},
  volume={6},
  number={2},
  pages={107--128},
  year={1981},
  doi = {10.3102/10769986006002107},
  publisher={Sage Publications Sage CA: Thousand Oaks, CA}
}

@article{cox1972regression,
  title={Regression models and life-tables},
  author={Cox, David R},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={34},
  number={2},
  pages={187--202},
  year={1972},
  doi = {https://doi.org/10.1111/j.2517-6161.1972.tb00899.x},
  url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1972.tb00899.x},
  publisher={Wiley Online Library}
}

@article{anderson2018baseline,
  title={Baseline Equivalence: What it is and Why it is Needed},
  author={Anderson, Mary Anne and Maxwell, Nan},
  journal={Submitted to AmeriCorps by Mathematica. Chicago, IL, September},
  url = {https://www.mathematica.org/-/media/publications/pdfs/labor/2021/cncs_baseline-equivalencebrief.pdf},
  year={2018}
}

@misc{li2022principal,
  author = {Fan Li},
  title = {STA 640 — Causal Inference Unit 6.2: Post-treatment confounding: Principal Stratification},
  year = {2022},
  url = {https://www2.stat.duke.edu/~fl35/teaching/640/Chapter6.2_principal%20stratification.pdf}
}

@article{angrist1996identification,
  title={Identification of causal effects using instrumental variables},
  author={Angrist, Joshua D and Imbens, Guido W and Rubin, Donald B},
  journal={Journal of the American statistical Association},
  volume={91},
  number={434},
  pages={444--455},
  year={1996},
  publisher={Taylor \& Francis},
  doi = {10.1080/01621459.1996.10476902}
}

@article{frangakis2002principal,
  title={Principal stratification in causal inference},
  author={Frangakis, Constantine E and Rubin, Donald B},
  journal={Biometrics},
  volume={58},
  number={1},
  pages={21--29},
  year={2002},
  publisher={Oxford University Press},
  doi = {10.1111/j.0006-341X.2002.00021.x},
  url = {https://doi.org/10.1111/j.0006-341X.2002.00021.x},
  eprint = {https://academic.oup.com/biometrics/article-pdf/58/1/21/51699091/biometrics\_58\_1\_21.pdf}
}

@techreport{imbens2014instrumental,
  title={Instrumental variables: An econometrician's perspective},
  author={Imbens, Guido},
  year={2014},
  institution={National Bureau of Economic Research},
  doi= {10.3386/w19983}
}

@article{imbens1997bayesian,
 ISSN = {00905364},
 URL = {http://www.jstor.org/stable/2242722},
 author = {Guido W. Imbens and Donald B. Rubin},
 journal = {The Annals of Statistics},
 number = {1},
 pages = {305--327},
 publisher = {Institute of Mathematical Statistics},
 title = {Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance},
 urldate = {2024-08-06},
 volume = {25},
 year = {1997}
}

@article{liu2023pstrata,
      title={PStrata: An R Package for Principal Stratification}, 
      author={Bo Liu and Fan Li},
      year={2023},
      eprint={2304.02740},
      archivePrefix={arXiv},
      primaryClass={stat.CO},
      url={https://arxiv.org/abs/2304.02740}
}

@article{vanderweele2011principal,
  title={Principal stratification--uses and limitations},
  author={VanderWeele, Tyler J},
  journal={The international journal of biostatistics},
  volume={7},
  number={1},
  pages={0000102202155746791329},
  year={2011},
  publisher={De Gruyter},
  doi = {10.2202/1557-4679.1329}
}

@manual{mc-stan2024finite,
  title        = {Finite Mixtures and Zero-inflated Models},
  author       = {Stan Development Team},
  year         = {2024},
  url          = {https://mc-stan.org/docs/stan-users-guide/finite-mixtures.html#zero-inflated.section},
  note         = {Accessed: 2024-09-29}
}

@misc{heiss2022hurdle,
  author       = {Andrew Heiss},
  title        = {A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models},
  year         = {2022},
  url          = {https://www.andrewheiss.com/blog/2022/05/09/hurdle-lognormal-gaussian-brms/},
  note         = {Accessed: 2024-09-29}
}

@article{zarin2005trial,
  title={Trial registration at ClinicalTrials. gov between May and October 2005},
  author={Zarin, Deborah A and Tse, Tony and Ide, Nicholas C},
  journal={New England Journal of Medicine},
  volume={353},
  number={26},
  pages={2779--2787},
  year={2005},
  publisher={Mass Medical Soc}
}