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Causal Inference

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Simulation is used to illuminate causal inference. We begin with a short look at causal graphs and potential outcomes. We then aim to understand and see examples of experiments, regression adjustment, matching and sensitivity analysis, regression discontinuity, difference-in-difference, Manski bounds and instrumental variables.


  • causality
  • experiments
  • matching
  • regression discontinuity
  • difference-in-difference
  • Manski bounds
  • instrumental variables

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  • DOI: 10.1007/978-981-15-2035-8_10
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  • Abadie, A., and M.D. Catteneo. 2018. Econometric methods for program evaluation. Annual Review of Economics 10: 465–503.

    Google Scholar 

  • Angrist, J.D., and J. Pischke. 2015. Mastering ‘metrics - The path from cause to effect. Princeton: Princeton University Press.

    MATH  Google Scholar 

  • Chattopadhyay, R., and E. Duflo. 2004. Women as policy makers: Evidence from a randomized policy experiment in India. Econometrica 72 (5): 1409–1443.

    MathSciNet  CrossRef  Google Scholar 

  • Coppock, A. 2019. ri2: Randomization inference for randomized experiments. R package version 0.1.2.

  • Dahl, D.B., D. Scott, C. Roosen, A. Magnusson, and J. Swinton. 2019. xtable: Export Tables to LaTeX or HTML. R package version 1.8-4.

  • Dehejia, R.H., and S. Wahba. 1999. Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association 94 (448): 1053–1062.

    CrossRef  Google Scholar 

  • Elwert, F. 2013. Graphical causal models. In Handbook of causal analysis for social research, ed. S.L. Morgan, 245–274. New York: Springer.

    CrossRef  Google Scholar 

  • Freedman, D.A. 1983. A note on screening regression equations. The American Statistician 37 (2): 152–155.

    MathSciNet  Google Scholar 

  • Gelman, A., and J. Hill. 2007. Data analysis using regression and multilevel/hierarchical models (Analytical methods for social research). Cambridge: Cambridge University Press.

    Google Scholar 

  • Greifer, N. 2019. cobalt: Covariate balance tables and plots. R package version 3.8.0.

  • Hill, R.C., W.E. Griffiths, and G.C. Lim. 2018. Principles of econometrics. New York: Wiley.

    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.

  • Imai, K. 2018. Quantitative social science - An introduction. Princeton: Princeton University Press.

    Google Scholar 

  • Josselin, J.-M., and B. Le Maux. 2017. Statistical tools for program evaluation: Methods and applications to economic policy, public health, and education. Berlin: Springer.

    CrossRef  Google Scholar 

  • Kahneman, D. 2011. Thinking, fast and slow. London: Penguin Books.

    Google Scholar 

  • Keele, L.J. 2014. rbounds: Perform Rosenbaum bounds sensitivity tests for matched and unmatched data. R package version 2.1.

  • Lalonde, R.J. 1986. Evaluating the econometric evaluations of training programs with experimental data. The American Economic Review 76 (4): 604–620.

    Google Scholar 

  • Leifeld, P. 2013. texreg: Conversion of statistical model output in R to LaTeX and HTML tables. Journal of Statistical Software 55 (8): 1–24.

  • Manski, C.F., and J.V. Pepper. 2018. How do right-to-carry laws affect crime rates? Coping with ambiguity using bounded variation assumptions. Review of Economics and Statistics 100 (2): 232–244.

    CrossRef  Google Scholar 

  • Meyer, B.D., W.K. Viscusi, and D.L. Durbin. 1995. Workers’ compensation and injury duration: Evidence from a natural experiment. The American Economic Review 85 (3): 322–340.

    Google Scholar 

  • Morgan, S.L., and C. Winship. 2014. Counterfactuals and causal inference: Methods and principles for social research (Analytical methods for social research). Cambridge: Cambridge University Press.

    CrossRef  Google Scholar 

  • Pearl, J., M. Glymour, and N.P. Jewell. 2016. Causal inference in statistics: A primer. New York: Wiley.

    MATH  Google Scholar 

  • Rosenbaum, P. 2005. Sensitivity analysis in observational studies. In Encyclopedia of statistics in behavioural science, ed. B.S. Everitt, D.C. Howell, 1809–1814. New York: Wiley.

    Google Scholar 

  • Rosenbaum, P. 2017. Observation and experiment - An introduction to causal inference. London: Harvard University Press.

    CrossRef  Google Scholar 

  • Rubin, D.B. 2008. Statistical inference for causal effects, with emphasis on applications in epidemiology and medical statistics. In Handbook of Statistics, vol. 27, ed. C.R. Rao, J.P. Miller, D.C. Rao. 2008. Amsterdam: Elsevier.

    Google Scholar 

  • Sekhon, J.S. 2011. Multivariate and propensity score matching software with automated balance optimization: The matching package for R.

    Google Scholar 

  • Stigler, M., and B. Quast. 2015. rddtools: Toolbox for Regression Discontinuity Design (‘RDD’). R package version 0.4.0.

  • Stock, J.H., and M.W. Watson. 2011. Introduction to econometrics. Boston: Addison-Wesley.

    Google Scholar 

  • Wooldridge, J. 2013. Introductory econometrics: A modern approach. Delhi: Cengage.

    Google Scholar 

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Correspondence to Vikram Dayal .

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Dayal, V. (2020). Causal Inference. In: Quantitative Economics with R. Springer, Singapore.

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