Causal Inference Reading Group

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Statistics Department Causal Inference Reading Group

This page is meant to help organize the Causal Inference Reading Group. If anyone is the group notices that it is not up to date, feel free to update it or let Mike know. This will be updated with additional information as it makes sense to do so.


Papers we have covered thus far:

  • "Linear Models: A Useful “Microscope” for Causal Analysis", J. Pearl; Journal of Causal Inference, (2013)
  • "Causal Inference in Statistics: An overview", J. Pearl (2009)
    • "Propensity Scores An Introduction and Experimental Test.", Luellen, Jason K., William R. Shadish, and M. H. Clark; Evaluation Review (2005)
    • "An introduction to instrumental variables for epidemiologists.", Greenland, Sander; International journal of epidemiology (2000)
  • "Causal Inference Using Potential Outcomes", D Rubin; Journal of the American Statistical Association, (2005)
  • "Active Learning of Causal Bayes Net Structure", K Murphy; (2001)
  • "Inferring Causal Impact Using Bayesian Structural Time-Series Models.", Brodersen, Kay H., et al.; Google Technical Report (2013).
  • "Some recent development in a concept of causality.", Granger, Clive WJ.; Journal of econometrics (1988)
  • "Reducing bias through directed acyclic graphs.", Shrier, Ian, and Robert W. Platt.; BMC medical research methodology (2008)
  • "Directed acyclic graphs helped to identify confounding in the association of disability and electrocardiographic findings: results from the KORA-Age study.", Röhrig, Nadine, et al.; Journal of clinical epidemiology (2014)
  • "Direct and Indirect Effects of Fish on Invertebrates and Tiger Salamanders in Prairie Pothole Wetlands.", Maurer, Kristine M., Timothy W. Stewart, and Frederick O. Lorenz.; Wetlands (2014)
  • "Direct and Indirect Causal Effects via Potential Outcomes.", Rubin, Donald B.; Scandinavian Journal of Statistics (2004)