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Articles

Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes

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Pages 85-96 | Received 17 Sep 2021, Accepted 01 Mar 2022, Published online: 25 Apr 2022

References

  • Bååth, R. (2015), “Probable Points and Credible Intervals, Part 2: Decision Theory,” Publishable Stuff: Rasmus Bååth’s Research Blog. Accessed May 27, 2019. Available at http://www.sumsar.net/blog/2015/01/probable-points-and-credible-intervals-parttwo/.
  • Baio, G., Berardi, A., and Heath, A. (2017), Bayesian Cost-Effectiveness Analysis with the R Package BCEA, Cham: Springer.
  • Bakaki, Z., and Böhmelt, T. (2021), “Can UN Peacekeeping Promote Environmental Quality?” International Studies Quarterly, 65, 881–890. DOI: 10.1093/isq/sqab051.
  • Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis (2nd ed.), New York, NY: Springer.
  • Carter, D. B., and Signorino, C. S. (2010), “Back to the Future: Modeling Time Dependence in Binary Data,” Political Analysis, 18, 271–292. DOI: 10.1093/pan/mpq013.
  • Cecchetti, S. G. (2000), “Making Monetary Policy: Objectives and Rules,” Oxford Review of Economic Policy, 16, 43–59. DOI: 10.1093/oxrep/16.4.43.
  • Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences (2nd ed.), Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Duncan, G. J., and Magnuson, K. 2007, “Penny Wise and Effect Size Foolish,” Child Development Perspectives, 1, 46–51.
  • Esarey, J., and Danneman, N. (2015), “A Quantitative Method for Substantive Robustness Assessment,” Political Science Research and Methods, 3, 95–111. DOI: 10.1017/psrm.2014.14.
  • Esarey, J., and Wu, A. (2016), “Measuring the Effects of Publication Bias in Political Science,” Research and Politics, 3, 1–9.
  • French, S., and Argyris, N. (2018), “Decision Analysis and Political Processes,” Decision Analysis, 15, 208–222. DOI: 10.1287/deca.2018.0374.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B, Vehtari, A., and Rubin, D. B. (2013), Bayesian Data Analysis (3rd ed.), Boca Raton, FL: CRC Press.
  • Gerber, A., and Malhotra, N. (2008), “Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals,” Quarterly Journal of Political Science, 3, 313–326. DOI: 10.1561/100.00008024.
  • Gilboa, I. (2009), Theory of Decision under Uncertainty, Cambridge: Cambridge University Press.
  • Gill, J. (2015), Bayesian Methods: A Social and Behavioral Sciences Approach (3rd ed.), Boca Raton, FL: CRC Press.
  • Goodrich, B., Gabry, J., Ali, I., and Brilleman, S. (2020), “rstanarm: Bayesian Applied Regression Modeling via Stan,” R package version 2.21.1. https://mcstan.org/rstanarm.
  • Gross, J. H. (2015), “Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance,” American Journal of Political Science, 59, 775–788. DOI: 10.1111/ajps.12149.
  • Harris, D. N. (2009), “Toward Policy-Relevant Benchmarks for Interpreting Effect Sizes: Combining Effects with Costs,” Educational Evaluation and Policy Analysis, 31, 3–29. DOI: 10.3102/0162373708327524.
  • Hernán, M. A., and Robins, J. M. (2020), Causal Inference: What If, Boca Raton, FL: Chapman and Hall/CRC.
  • Horowitz, A. R. (1987), “Loss Functions and Public Policy,” Journal of Macroeconomics, 9, 489–504. DOI: 10.1016/0164-0704(87)90016-4.
  • Iacus, S. M., King, G., and Porro, G. (2012), “Causal Inference without Balance Checking: Coarsened Exact Matching,” Political Analysis, 20, 1–24. DOI: 10.1093/pan/mpr013.
  • Imai, K., Keele, L., and Yamamoto, T. (2010), “Identification, Inference and Sensitivity Analysis for Causal Mediation Effects,” Statistical Science, 25, 51–71. DOI: 10.1214/10-STS321.
  • Kahneman, D., and Tversky, A. (1979), “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47, 263–292. DOI: 10.2307/1914185.
  • Kruschke, J. K. (2018), “Rejecting or Accepting Parameter Values in Bayesian Estimation,” Advances in Methods and Practices in Psychological Science, 1, 270–280. DOI: 10.1177/2515245918771304.
  • Laber, E. B., and Shedden, K. (2017), “Statistical Significance and the Dichotomization of Evidence: The Relevance of the ASA Statement on Statistical Significance and p-Values for Statisticians,” Journal of the American Statistical Association, 112, 902–904. DOI: 10.1080/01621459.2017.1311265.
  • Manski, C. F. (2013), Public Policy in an Uncertain World: Analysis and Decisions, Cambridge, MA: Harvard University Press.
  • Manski, C. F. (2019), “Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing,” The American Statistician, 73, 296–304.
  • Mayer, T. (2003), “The Macroeconomic Loss Function: A Critical Note,” Applied Economics Letters, 10, 347–349. DOI: 10.1080/1350485032000056891.
  • McNeil, B. J., and Pauker, S. G. (1984), “Decision Analysis for Public Health: Principles and Illustrations,” Annual Review of Public Health, 5, 135–61. DOI: 10.1146/annurev.pu.05.050184.001031.
  • McShane, B. B., and Gal, D. (2016), “Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence,” Management Science, 62, 1707–1718. DOI: 10.1287/mnsc.2015.2212.
  • McShane, B. B., and Gal, D. (2017), “Statistical Significance and the Dichotomization of Evidence,” Journal of the American Statistical Association, 112, 885–895.
  • Montgomery, J. M., and Nyhan, B. (2010), “Bayesian Model Averaging: Theoretical Developments and Practical Applications,” Political Analysis, 18, 245–270. DOI: 10.1093/pan/mpq001.
  • Morgan, S. L., and Winship, C. (2015), Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.), Cambridge: Cambridge University Press.
  • Mudge, J. F., Baker, L. F., Edge, C. B., and Houlahan, J. E. (2012), “Setting an Optimal α That Minimizes Errors in Null Hypothesis Significance Tests,” PloS One, 7, 1–7. DOI: 10.1371/journal.pone.0032734.
  • Nielsen, R. A., Findley, M. G., Davis, Z. S., Candland, T., and Nielson, D. L. (2011), “Foreign Aid Shocks as a Cause of Violent Armed Conflict,” American Journal of Political Science, 55, 219–232. DOI: 10.1111/j.1540-5907.2010.00492.x.
  • Nordås, R., and Rustad, S. C. A. (2013), “Sexual Exploitation and Abuse by Peacekeepers: Understanding Variation,” International Interactions, 39, 511–534. DOI: 10.1080/03050629.2013.805128.
  • Pearl, J., Glymour, M., and Jewell, N. P. (2016), Causal Inference in Statistics: A Primer, Chichester: Wiley.
  • Quinn, J. M., Mason, T. D., and Gurses, M. (2007), “Sustaining the Peace: Determinants of Civil War Recurrence,” International Interactions, 33, 167–193. DOI: 10.1080/03050620701277673.
  • R Core Team. (2021), R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org.
  • Rapoport, A. (1998), Decision Theory and Decision Behaviour (2nd ed.), Hampshire: Macmillan Press Ltd.
  • Reeder, B. W., and Polizzi, M. S. (2021), “Transforming Zones of Exclusion to Zones of Inclusion? Local-Level UN Peacekeeping Deployments and Educational Attainment,” International Studies Quarterly, 65, 867–880. DOI: 10.1093/isq/sqab018.
  • Robert, C. P. (2007), The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (2nd ed.), New York, NY: Springer.
  • RStudio Team. (2020), RStudio: Integrated Development for R, Boston, MA: RStudio, PBC. Available at http://www.rstudio.com/.
  • Ruggeri, A., Dorussen, H., and Gizelis, T.-I. (2017), “Winning the Peace Locally: UN Peacekeeping and Local Conflict,” International Organization, 71, 163–185. DOI: 10.1017/S0020818316000333.
  • Signorino, C. S. (1999), “Strategic Interaction and the Statistical Analysis of International Conflict,” American Political Science Review, 93, 279–297. DOI: 10.2307/2585396.
  • Simonsohn, U., Nelson, l. D., and Simmons, J. P. (2014), “P-Curve: A Key to the File-Drawer,” Journal of Experimental Psychology: General, 143, 534–547. DOI: 10.1037/a0033242.
  • Stan Development Team. (2019), Stan Reference Manual, Version 2.27. https://mc-stan.org/docs/2 27/reference-manual/index.html.
  • Suzuki, A. (2020), “Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes,” arXiv: 2008.10903 [stat.ME]. https://arxiv.org/abs/2008.10903.
  • Suzuki, A. (2022), “Presenting the Probabilities of Different Effect Sizes: Towards a Better Understanding and Communication of Statistical Uncertainty,” arXiv: 2008.07478v3 [stat.AP]. https://arxiv.org/abs/2008.07478.
  • Taleb, N. N. (2007), The Black Swan: The Impact of the Highly Improbable, New York, NY: Random House.
  • Tellez, J. F. (2019), “Peace Agreement Design and Public Support for Peace: Evidence from Colombia,” Journal of Peace Research, 56, 827–844. DOI: 10.1177/0022343319853603.
  • Tetenov, A. (2012), “Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria,” Journal of Econometrics, 166, 157–165. DOI: 10.1016/j.jeconom.2011.06.013.
  • Walpole, H. D., and Wilson, R. S. (2020), “Extending a Broadly Applicable Measure of Risk Perception: The Case for Susceptibility,” Journal of Risk Research, 24, 135–147. DOI: 10.1080/13669877.2020.1749874.
  • Warjiyo, P., and Juhro, S. M. (2019), Central Bank Policy: Theory and Practice, Bingley: Emerald Publishing Limited.
  • Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis (2nd ed.), Cham: Springer.