Graphical causal modelling: an application to identify and estimate cause-and-effect relationships

References

• Acharya, A., M. Blackwell, and M. Sen. 2016. “Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects.” The American Political Science Review 110 (3): 512–529. doi:10.1017/S0003055416000216.
   Web of Science ®Google Scholar
• Angrist, J. D., and A. B. Krueger. 1991. “Does Compulsory School Attendance Affect Schooling and Earnings?” The Quarterly Journal of Economics 106 (4): 979–1014. doi:10.2307/2937954.
   Web of Science ®Google Scholar
• Assunção, J., A. Bragança, and P. Hemsley. 2019. “Geographic Heterogeneity and Technology Adoption: Evidence from Brazil.” Land Economics 95 (4): 599–616. doi:10.3368/le.95.4.599.
   Web of Science ®Google Scholar
• Barenboim, E., and P. Hünermund 2019. Causal inference and data-fusion in econometrics. Accessed online January 2020, at https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=EEAESEM2019paper_id=491.
   Google Scholar
• Bellemare, M. F., J. R. Bloem, and N. Wexlar 2020. The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion. Accessed online January 2021, at https://www.canr.msu.edu/afre/events/Bellemare%20and%20Bloem%202020.pdf.
   Google Scholar
• Bellemare, M. F., T. Masaki, and T. B. Pepinsky. 2017. “Lagged Explanatory Variables and the Estimation of Causal Effect.” The Journal of Politics 79 (3): 949–963. doi:10.1086/690946.
   Web of Science ®Google Scholar
• Blackburn, M., and D. Neumark. 1995. “Are OLS Estimates of the Return to Schooling Biased Downward? Another Look.” The Review of Economics and Statistics 77 (2): 217–230. doi:10.2307/2109861.
   Web of Science ®Google Scholar
• Bound, J., D. A. Jaeger, and R. M. Baker. 1995. “Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak.” Journal of the American Statistical Association 90 (430): 443–450. doi:10.1080/01621459.1995.10476536.
   Web of Science ®Google Scholar
• Clay, K., J. Lingwall, and M. Stephens Jr. 2021. “Laws, Educational Outcomes, and Returns to Schooling Evidence from the First Wave of U.S. State Compulsory Attendance Laws.” Labour Economics 68: 1–10. doi:10.1016/j.labeco.2020.101935.
   Web of Science ®Google Scholar
• Cragg, S. G., and R. Uhler. 1970. “The Demand for Automobiles.” Canadian Journal of Economics 3 (3): 386–406. doi:10.2307/133656.
   Google Scholar
• Cunningham, S. 2021. Causal Inference: The Mixtape. New Haven, CT: Yale University Press.
   Google Scholar
• Dynarski, S. M. 2003. “Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion.” The American Economic Review 93 (1): 279–288. doi:10.1257/000282803321455287.
   Web of Science ®Google Scholar
• Elwert, F. 2013. “Graphical Causal Models.” In Handbook of Causal Analysis for Social Research, edited by S. L. Morgan, 245–273. Dordrecht, Norway: Springer Science + Business Media. Pagesof.
   Google Scholar
• Elwert, F., and C. Winship. 2010. “Effect Heterogeneity and Bias in Main-Effects-Only Regression Models.” In Heuristics, Probability and Causality: A Tribute to Judea Pearl, edited by R. Dechter, H. Geffner, and J. Y. Halpern, 327–336. London, UK: College Publications. Pagesof.
   Google Scholar
• Elwert, F., and C. Winship. 2014. “Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable.” Annual Review of Sociology 40 (1): 31–53. doi:10.1146/annurev-soc-071913-043455.
   PubMed Web of Science ®Google Scholar
• Feigenbaum, J. J., and H. R. Tan. 2019. “The Return to Education in the Mid-20th Century: Evidence from Twins.” NBER Working Paper Number 26407.
   Google Scholar
• Goldin, C., and L. Katz. 2000. “Education and Income in the Early Twentieth Century: Evidence from the Prairies.” The Journal of Economic History 60 (3): 782–818. doi:10.1017/S0022050700025766.
   Web of Science ®Google Scholar
• Greifer, N. 2022. Matching with sampling weights. MatchIt Vignette: ‘Sampling Weights,’ accessed online July 2022, https://cran.r-project.org/web/packages/MatchIt/vignettes/sampling-weights.html.
   Google Scholar
• Györfi, L., M. Kohler, A. Krzyżak, and H. Walk. 2002. A Distribution-Free Theory of Nonparametric Regression. New York: Springer-Verlag.
   Google Scholar
• Heckman, J., and R. Pinto. 2015. “Causal Analysis After Haavelmo.” Econometric Theory 31 (1): 115–151. doi:10.1017/S026646661400022X.
   PubMed Web of Science ®Google Scholar
• Ho, D. E., K. Imai, G. King, and E. A. Stuart. 2011. “MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.” Journal of Statistical Software 42 (8): 1–28. doi:10.18637/jss.v042.i08.
   Web of Science ®Google Scholar
• Holland, P. W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–960. doi:10.1080/01621459.1986.10478354.
   Web of Science ®Google Scholar
• Imbens, G. W. 2015. “Matching Methods in Practice: Three Examples.” The Journal of Human Resources 50 (2): 373–419. doi:10.3368/jhr.50.2.373.
   Web of Science ®Google Scholar
• Imbens, G. W. 2020. “Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics.” Journal of Economic Literature 58 (4): 1129–1179. doi:10.1257/jel.20191597.
   Web of Science ®Google Scholar
• Ji, Q., H. -Y. Zhang, and J. -B. Geng. 2018. “What Drives Natural Gas Prices in the United States? – a Directed Acyclic Graph Approach.” Energy Economics 69: 79–88. doi:10.1016/j.eneco.2017.11.002.
   Web of Science ®Google Scholar
• Keele, L. 2015. “The Statistics of Causal Inference: A View from Political Methodology.” Political Analysis 23 (3): 313–335. doi:10.1093/pan/mpv007.
   Web of Science ®Google Scholar
• Koster, J. T. A. 1996. “Markov Properties of Nonrecursive Causal Models.” Annals of Statistics 24 (5): 2148–2177. doi:10.1214/aos/1069362315.
   Web of Science ®Google Scholar
• Lumley, T. 2021. “Survey: Analysis of Complex Survey Samples.” R package, version 4. 1.
   Google Scholar
• Manski, C. F. 2007. Identification for Prediction and Decision. Cambridge, MA: Harvard University Press.
   Google Scholar
• Mincer, J. 1958. “Investment in Human Capital and Personal Income Distribution.” The Journal of Political Economy 66 (4): 281–302. doi:10.1086/258055.
   Web of Science ®Google Scholar
• Mincer, J. 1974. Schooling, Experience and Earnings. New York, NY: Columbia University Press.
   Google Scholar
• Morgan, S. L., and C. Winship. 2015. Counterfactuals and Causal Inference. 2 edn ed. New York, NY: Cambridge University Press.
   Google Scholar
• Moya, A. 2018. “Violence, Psychological Trauma, and Risk Attitudes: Evidence from Victims of Violence in Colombia.” Journal of Development Economics 131: 15–27. doi:10.1016/j.jdeveco.2017.11.001.
   Web of Science ®Google Scholar
• Murnane, R. J., and J. B. Willett. 2011. Methods Matter: Improving Causal Inference in Educational and Social Science Research. New York, NY: Oxford University Press.
   Google Scholar
• National Longitudinal Surveys. 2022. National Longitudinal Survey of Youth 1979. A Program of the U.S. Bureau of Labor Statistics, accessed online January 2022, https://www.nlsinfo.org/content/cohorts/nlsy79.
   Google Scholar
• Neyman, J. [1923] 1990. “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles.” Statistical Science 5 (4): 465–472. Section 9 Trans. D.M. Dabrowska and T.P. Speed.
   Google Scholar
• Oreopoulos, P., and K. Salvanes. 2011. “Priceless: The Nonpecuniary Benefits of Schooling.” Journal of Economic Perspectives 25 (1): 159–184. doi:10.1257/jep.25.1.159.
   Web of Science ®Google Scholar
• Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. 2 edn ed. Oxford, UK: Morgan Kaufmann Publishers, Inc.
   Google Scholar
• Pearl, J. 2009. Causality. 2 edn ed. New York, NY: Cambridge University Press.
   Google Scholar
• Pearl, J., M. Glymour, and N. P. Jewell. 2016. Causal Inference in Statistics: A Primer. West Sussex, UK: John Wiley & Son Ltd.
   Google Scholar
• Richardson, T. 1994. “Equivalence in non-recursive structural equation models.” In Compstat: Proceeding in Computation Statistics, edited by R. Dutter and W. Grossmann, 482–487. Berlin, Germany: Springer-Verlag.
   Google Scholar
• Robins, J. M., and L. Wasserman. 1999. “On the Impossibility of Inferring Causation from Association Without Background Knowledge.” In Computation, Causation, and Discovery, edited by C. N. Glymour and G. G. Cooper, 305–321. Cambridge, MA: MIT Press. Pagesof.
   Google Scholar
• Rosenbaum, P. R. 2010. Design of Observational Studies. New York, NY: Springer Science+Business Media.
   Google Scholar
• Rubin, D. B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5): 688–701. doi:10.1037/h0037350.
   Web of Science ®Google Scholar
• Scutari, M. 2010. “Learning Bayesian Networks with the Bnlearn R Package.” Journal of Statistical Software 35 (3): 1–22. doi:10.18637/jss.v035.i03.
   PubMed Web of Science ®Google Scholar
• Spirtes, P. 1993. “Directed Cyclic Graphs, Conditional Independence, and Non-Recursive Linear Structural Equation Models.” Department of Philosophy Technical Report CMU-Phil-35, Carnegie Mellon University.
   Google Scholar
• Spirtes, P. 2005. “Graphical Models, Causal Inference, and Econometric Models.” Journal of Economic Methodology 12 (1): 1–33. doi:10.1080/1350178042000330887.
   Google Scholar
• Staiger, D., and J. H. Stock. 1997. “Instrumental Variables Regression with Weak Instruments.” Econometrica 65 (3): 557–586. doi:10.2307/2171753.
   Web of Science ®Google Scholar
• Stephens, M., Jr, and D. -Y. Yang. 2014. “Compulsory Education and the Benefits of Schooling.” The American Economic Review 104 (6): 1777–1792. doi:10.1257/aer.104.6.1777.
   Web of Science ®Google Scholar
• Stuart, E. A., and K. M. Green. 2008. “Using Full Matching to Estimate Causal Effects in Nonexperimental Studies: Examining the Relationship Between Adolescent Marijuana Use and Adult Outcomes.” Developmental Psychology 44 (2): 395–406. doi:10.1037/0012-1649.44.2.395.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J., and J. M. Robins. 2007. “Four Types of Effect Modification: A Classification Based on Direct Acyclic Graphs.” Epidemiology 18 (5): 561–568. doi:10.1097/EDE.0b013e318127181b.
   PubMed Web of Science ®Google Scholar