1.6 Overview of some readings

• See syllabus for updated list.
• Hot literature…
• Essential readings on which this seminar is based
  • Keele, Luke. 2015. “The Statistics of Causal Inference: A View from Political Methodology.”
  • Keele, Luke. 2015. “The Discipline of Identification.”
  • Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
    • Why? Avoid conceptual/notational ambiguity!
  • Holland, Paul W. 1988. “Causal Inference, Path Analysis, and Recursive Structural Equations Models.”
  • Winship, C., & Sobel, M. 2004. “Causal inference in sociological studies.”⁷
  • Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering ’Metrics: The Path from Cause to Effect. With French flaps edition. Princeton University Press.
• Additional readings
  • EGAP Methods Guides
  • Holland, P. W.. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81(396), 945-960. http://doi.org/10.2307/2289064 (get here or here)
  • 10 Things You Need to Know About Causal Inference
  • 10 Things to Know About Covariate Adjustment
  • Rubin causal model on Wikipedia.
  • Great books to get an overview are Angrist and Pischke (2008) and Morgan and Winship (2007).
  • For an overview of the history both Holland (1986) and Barringer, Eliason, and Leahey (2013) seem suited.
  • For a discussion of structural equation modelling from a causal inference perspective check out Bollen and Pearl (2013).
  • For a discussion of mediation see Imai, Keele, and Tingley (2010).
  • A critique focusing on the value of qualitative data and observations is formulated by Freedman (1991).
  • Check out the handbook by Morgan (2013) for recent discussion of various issues (also configurational analysis).
• More interesting readings!
  • Validating vignette and conjoint survey experiments against real-world behavior (Hainmueller, Hangartner, and Yamamoto 2015)
  • Who gets a Swiss passport? A natural experiment in immigrant discrimination (Hainmueller and Hangartner 2013)

References

Angrist, Joshua D, and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, New Jersey: Princeton University Press.

Barringer, Sondra N, Scott R Eliason, and Erin Leahey. 2013. “A History of Causal Analysis in the Social Sciences.” In Handbook of Causal Analysis for Social Research, edited by Stephen L Morgan, 9–26. Springer Netherlands.

Bollen, Kenneth A, and Judea Pearl. 2013. “Eight Myths About Causality and Structural Equation Models.” In Handbook of Causal Analysis for Social Research, edited by Stephen L Morgan, 301–28. Handbooks of Sociology and Social Research. Springer Netherlands.

Freedman, David A. 1991. “Statistical Models and Shoe Leather.” Sociological Methodology 21 (2): 291–313.

Hainmueller, Jens, and Dominik Hangartner. 2013. “Who Gets a Swiss Passport? A Natural Experiment in Immigrant Discrimination.” The American Political Science Review 107 (01): 159–87.

Hainmueller, Jens, Dominik Hangartner, and Teppei Yamamoto. 2015. “Validating Vignette and Conjoint Survey Experiments Against Real-World Behavior.” Proceedings of the National Academy of Sciences of the United States of America 112 (8): 2395–2400.

Holland, Paul W. 1986. “Statistics and Causal Inference.” J. Am. Stat. Assoc. 81 (396): 945–60.

Imai, Kosuke, Luke Keele, and Dustin Tingley. 2010. “A General Approach to Causal Mediation Analysis.” Psychological Methods 15 (4): 309–34.

Morgan, Stephen L. 2013. Handbook of Causal Analysis for Social Research: Springer Netherlands.

Morgan, Stephen L, and Christopher Winship. 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge, UK: Cambridge University Press.

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7. Ideas: Negative sides effects.. Causalism vs. prediction (e.g. Demographers) and process tracing (e.g. Ethnographer or Psychologist); Parallel developments: Sociologists/Psychometricians/Econometricians SEM vs. Statisticians RCM (distinction not quite so clear). “our primary purposesare to introduce sociologists to the literaturethat uses a counterfactual notion of causalityand to illustrate some strategies for obtainingmore credible estimates of causal effects. Inaddition (and perhaps more importantly), webelieve that widespread understanding of thisliterature should result in important changesin the way that virtually all empirical work in sociology is conducted” (Winship and Sobel 2004, 483). They introduce counterfactual model, unit effects, average effects, sources of bias, randomized experiments, ignorability, estimation of causal effects etc.↩