22 References
Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility,
Data Requirements, and Methodological Aspects.” Journal of
Economic Literature 59 (2): 391–425.
Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2015.
“Comparative Politics and the Synthetic Control Method.”
American Journal of Political Science 59 (2): 495–510.
Abadie, Alberto, and Jaume Vives-i-Bastida. 2022. “Synthetic
Controls in Action.” https://arxiv.org/abs/2203.06279.
Anderson, Mary Anne, and Nan Maxwell. 2018. “Baseline Equivalence:
What It Is and Why It Is Needed.” Submitted to AmeriCorps by
Mathematica. Chicago, IL, September. https://www.mathematica.org/-/media/publications/pdfs/labor/2021/cncs_baseline-equivalencebrief.pdf.
Angrist, Joshua D, Guido W Imbens, and Donald B Rubin. 1996.
“Identification of Causal Effects Using Instrumental
Variables.” Journal of the American Statistical
Association 91 (434): 444–55. https://doi.org/10.1080/01621459.1996.10476902.
Athey, Susan, Raj Chetty, Guido W Imbens, and Hyunseung Kang. 2019.
“The Surrogate Index: Combining Short-Term Proxies to Estimate
Long-Term Treatment Effects More Rapidly and Precisely.” National
Bureau of Economic Research.
Athey, Susan, and Guido W Imbens. 2017. “The State of Applied
Econometrics: Causality and Policy Evaluation.” Journal of
Economic Perspectives 31 (2): 3–32.
Bagby, Emilie, and Anu Rangarajan. 2023. Using
Rapid-Cycle Evaluation to Improve Program Design and
Delivery. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190059668.013.7.
Brodersen, Kay H, Fabian Gallusser, Jim Koehler, Nicolas Remy, and
Steven L Scott. 2015. “Inferring Causal Impact Using Bayesian
Structural Time-Series Models.” Annals of Applied
Statistics 9: 247–74. https://doi.org/10.1214/14-AOAS788.
Bürkner, Paul-Christian. 2017. “brms:
An R Package for Bayesian Multilevel Models
Using Stan.” Journal of Statistical
Software 80 (1): 1–28. https://doi.org/10.18637/jss.v080.i01.
Carpenter, Bob, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben
Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li,
and Allen Riddell. 2017. “Stan: A Probabilistic Programming
Language.” Journal of Statistical Software 76 (1): 1–32.
https://doi.org/10.18637/jss.v076.i01.
Chandler, Jesse J, Ignacio Martinez, Mariel M Finucane, Jeffrey G
Terziev, and Alexandra M Resch. 2020. “Speaking on Data’s Behalf:
What Researchers Say and How Audiences Choose.” Evaluation
Review 44 (4): 325–53.
Chen, Jiafeng, and David M Ritzwoller. 2023. “Semiparametric
Estimation of Long-Term Treatment Effects.” Journal of
Econometrics 237 (2): 105545.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler,
and Vasilis Syrgkanis. 2024. “Applied Causal Inference Powered by
ML and AI” 12 (1): 338. https://causalml-book.org/assets/chapters/CausalML_chap_2.pdf.
Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010.
“BART: Bayesian additive regression
trees.” The Annals of Applied Statistics 4 (1):
266–98. https://doi.org/10.1214/09-AOAS285.
Cox, David R. 1972. “Regression Models and Life-Tables.”
Journal of the Royal Statistical Society: Series B
(Methodological) 34 (2): 187–202. https://doi.org/https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.
Cunningham, Scott. 2021. “Potential Outcomes Causal Model.”
In Causal Inference: The Mixtape. Yale University Press. https://mixtape.scunning.com/04-potential_outcomes.
Ding, Peng, and Fan Li. 2018. “Causal Inference.”
Statistical Science 33 (2): 214–37. https://projecteuclid.org/journals/statistical-science/volume-33/issue-2/Causal-Inference-A-Missing-Data-Perspective/10.1214/18-STS645.pdf.
Duke, Annie. 2019. Thinking in Bets: Making Smarter Decisions When
You Don’t Have All the Facts. Penguin. https://www.google.com/books/edition/Thinking_in_Bets/CI-RDwAAQBAJ.
Finucane, Mariel McKenzie, Ignacio Martinez, and Scott Cody. 2018.
“What Works for Whom? A Bayesian Approach to Channeling Big Data
Streams for Public Program Evaluation.” American Journal of
Evaluation 39 (1): 109–22. https://journals.sagepub.com/doi/abs/10.1177/1098214017737173.
Frangakis, Constantine E, and Donald B Rubin. 2002. “Principal
Stratification in Causal Inference.” Biometrics 58 (1):
21–29. https://doi.org/10.1111/j.0006-341X.2002.00021.x.
Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D.
B. Rubin. 2013. Bayesian Data Analysis, Third Edition. Chapman
& Hall/CRC Texts in Statistical Science. Taylor & Francis. http://www.stat.columbia.edu/~gelman/book/.
Gigerenzer, Gerd, Stefan Krauss, and Oliver Vitouch. 2004. “The
Null Ritual.” The Sage Handbook of Quantitative Methodology
for the Social Sciences, 391–408.
Goodrich, Ben, Jonah Gabry, Imad Ali, and Sam Brilleman. 2020.
“Rstanarm: Bayesian Applied Regression Modeling via
Stan.” https://mc-stan.org/rstanarm.
Hahn, P Richard, Jared S Murray, and Carlos M Carvalho. 2020.
“Bayesian Regression Tree Models for Causal Inference:
Regularization, Confounding, and Heterogeneous Effects (with
Discussion).” Bayesian Analysis 15 (3): 965–1056. https://doi.org/10.1214/19-BA1195.
He, Jingyu, Saar Yalov, and P Richard Hahn. 2019. “XBART:
Accelerated Bayesian Additive Regression Trees.” In The 22nd
International Conference on Artificial Intelligence and Statistics,
1130–38. PMLR. https://proceedings.mlr.press/v89/he19a.html.
Hedges, Larry V. 1981. “Distribution Theory for Glass’s Estimator
of Effect Size and Related Estimators.” Journal of
Educational Statistics 6 (2): 107–28. https://doi.org/10.3102/10769986006002107.
Heiss, Andrew. 2022. “A Guide to Modeling Outcomes That Have Lots
of Zeros with Bayesian Hurdle Lognormal and Hurdle Gaussian Regression
Models.” https://www.andrewheiss.com/blog/2022/05/09/hurdle-lognormal-gaussian-brms/.
Herren, Drew, Richard Hahn, Jared Murray, Carlos Carvalho, and Jingyu
He. 2024. Stochtree: Stochastic Tree Ensembles (XBART and BART) for
Supervised Learning and Causal Inference. https://stochastictree.github.io/stochtree-r/.
Hill, Jennifer L. 2011. “Bayesian Nonparametric Modeling for
Causal Inference.” Journal of Computational and Graphical
Statistics 20 (1): 217–40. https://doi.org/10.1198/jcgs.2010.08162.
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2011.
“MatchIt: Nonparametric Preprocessing for Parametric
Causal Inference.” Journal of Statistical Software 42
(8): 1–28. https://doi.org/10.18637/jss.v042.i08.
Ho, Daniel E, Kosuke Imai, Gary King, and Elizabeth A Stuart. 2007.
“Matching as Nonparametric Preprocessing for Reducing Model
Dependence in Parametric Causal Inference.” Political
Analysis 15 (3): 199–236.
Hoekstra, Rink, Richard D Morey, Jeffrey N Rouder, and Eric-Jan
Wagenmakers. 2014. “Robust Misinterpretation of Confidence
Intervals.” Psychonomic Bulletin & Review 21:
1157–64. https://doi.org/10.3758/s13423-013-0572-3.
Holland, Paul W. 1986. “Statistics and Causal Inference.”
Journal of the American Statistical Association 81 (396):
945–60. https://doi.org/10.2307/2289064.
Imbens, Guido. 2014. “Instrumental Variables: An Econometrician’s
Perspective.” National Bureau of Economic Research. https://doi.org/10.3386/w19983.
Imbens, Guido W., and Donald B. Rubin. 1997. “Bayesian Inference
for Causal Effects in Randomized Experiments with Noncompliance.”
The Annals of Statistics 25 (1): 305–27. http://www.jstor.org/stable/2242722.
Imbens, Guido, Nathan Kallus, Xiaojie Mao, and Yuhao Wang. 2022.
“Long-Term Causal Inference Under Persistent Confounding via Data
Combination.” arXiv Preprint arXiv:2202.07234.
Kassler, Daniel, Ira Nichols-Barrer, and Mariel Finucane. 2018.
“Beyond ‘Treatment Versus Control’: How Bayesian
Analysis Makes Factorial Experiments Feasible in Education
Research.” https://doi.org/10.1177/0193841X18818903.
King, Gary, and Richard Nielsen. 2019. “Why Propensity Scores
Should Not Be Used for Matching.” Political Analysis 27
(4): 435–54. https://doi.org/10.1017/pan.2019.11.
Krantsevich, Nikolay, Jingyu He, and P. Richard Hahn. 2023.
“Stochastic Tree Ensembles for Estimating Heterogeneous
Effects.” In Proceedings of the 26th International Conference
on Artificial Intelligence and Statistics, edited by Francisco
Ruiz, Jennifer Dy, and Jan-Willem van de Meent, 206:6120–31. Proceedings
of Machine Learning Research. PMLR. https://proceedings.mlr.press/v206/krantsevich23a.html.
Kruschke, John K, and Torrin M Liddell. 2018. “Bayesian Data
Analysis for Newcomers.” Psychonomic Bulletin &
Review 25 (1): 155–77. https://doi.org/10.3758/s13423-017-1272-1.
Li, Fan. 2022. “STA 640 — Causal Inference Unit 6.2:
Post-Treatment Confounding: Principal Stratification.” https://www2.stat.duke.edu/~fl35/teaching/640/Chapter6.2_principal%20stratification.pdf.
Li, Fan, Peng Ding, and Fabrizia Mealli. 2023. “Bayesian Causal
Inference: A Critical Review.” Philosophical Transactions of
the Royal Society A 381 (2247): 20220153. https://doi.org/10.1098/rsta.2022.0153.
Liu, Bo, and Fan Li. 2023. “PStrata: An r Package for Principal
Stratification.” https://arxiv.org/abs/2304.02740.
Manski, Charles F. 2020. “The Lure of Incredible
Certitude.” Economics & Philosophy 36 (2): 216–45.
https://www.nber.org/system/files/working_papers/w24905/w24905.pdf.
Martinez, Ignacio, and Jaume Vives-i-Bastida. 2023. “Bayesian and
Frequentist Inference for Synthetic Controls.” https://arxiv.org/abs/2206.01779.
McElreath, R. 2018a. Statistical Rethinking: A Bayesian Course with
Examples in r and Stan. Chapman & Hall/CRC Texts in Statistical
Science. CRC Press. https://books.google.com/books?id=T3FQDwAAQBAJ.
———. 2018b. Statistical Rethinking: A Bayesian Course with Examples
in r and Stan. Chapman &Amp; Hall/CRC Texts in Statistical
Science. CRC Press. https://books.google.com/books?id=T3FQDwAAQBAJ.
Neyman, Jersey. 1923. “Sur Les Applications de La
Théorie Des Probabilités Aux Experiences
Agricoles: Essai Des Principes.” Roczniki Nauk
Rolniczych 10 (1): 1–51.
Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in
Randomized and Nonrandomized Studies.” Journal of Educational
Psychology 66 (5): 688. http://www.fsb.muohio.edu/lij14/420_paper_Rubin74.pdf.
———. 1978. “Bayesian Inference for Causal Effects: The Role of
Randomization.” The Annals of Statistics, 34–58. https://www.jstor.org/stable/2958688.
Rubin, Donald B. 1984. “Bayesianly
Justifiable and Relevant Frequency Calculations for the Applied
Statistician.” The Annals of Statistics 12 (4):
1151–72. https://doi.org/10.1214/aos/1176346785.
Schloerke, Barret, Winston Chang, George Stagg, and Garrick Aden-Buie.
2024. Shinylive: Run ’Shiny’ Applications in the Browser. https://posit-dev.github.io/r-shinylive/.
Team, Stan Development. 2024. Finite Mixtures and Zero-Inflated
Models. https://mc-stan.org/docs/stan-users-guide/finite-mixtures.html#zero-inflated.section.
Thal, Dan RC, and Mariel M Finucane. 2023. “Causal Methods
Madness: Lessons Learned from the 2022 ACIC Competition to Estimate
Health Policy Impacts.” Observational Studies 9 (3):
3–27. https://doi.org/110.1353/obs.2023.0023.
Thaler, Richard H, and Cass R Sunstein. 2009. Nudge: Improving
Decisions about Health, Wealth, and Happiness. Penguin.
———. 2021. Nudge: The Final Edition. Yale University Press.
VanderWeele, Tyler J. 2011. “Principal Stratification–Uses and
Limitations.” The International Journal of Biostatistics
7 (1): 0000102202155746791329. https://doi.org/10.2202/1557-4679.1329.
Wang, Meijia, Ignacio Martinez, and P Richard Hahn. 2024.
“LongBet: Heterogeneous Treatment Effect Estimation in Panel
Data.” arXiv Preprint arXiv:2406.02530. https://doi.org/arXiv:2406.02530.
Wasserstein, Ronald L, and Nicole A Lazar. 2016. “The ASA
Statement on p-Values: Context, Process, and Purpose.” The
American Statistician. Taylor & Francis. https://www.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108.
WWC. 2020. “What Works Clearinghouse Baseline Equivalence
Standard.” U.S. Department of Education, Institute of Education
Sciences, National Center for Education Evaluation; Regional Assistance.
https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/WWC-Baseline-Brief-v6_508.pdf.
Zhang, Vickie, Michael Zhao, Anh Le, and Nathan Kallus. 2023.
“Evaluating the Surrogate Index as a Decision-Making Tool Using
200 a/b Tests at Netflix.” arXiv Preprint
arXiv:2311.11922.