Course Readings

Suggested background readings for each topic/section of the course are provided below. Helpful references are also provided at appropriate points in the lecture slides. Please note that the listed references are NOT intended as a complete bibliography, but only as helpful entry points to the material. 

Core Texts

M.J. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer, Berlin Heidelberg New York, 2011. 

- Book website:  http://www.targetedlearningbook.com/

J. Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, New York, 2nd edition, 2009. 

- Book website: http://bayes.cs.ucla.edu/BOOK-2K/

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Additional relevant texts

  • Hernan and Robins. Causal Inference. 2018. (In process).

  • Freedman. Statistical Methods and Causal Inference: A dialogue with the social sciences. 2010.

    • A series of essays relevant to causal inference.

  • Morgan and Winship. Counterfactuals and Causal Inference. Methods and Principles for the Social Sciences. 2007.

    • Overview of the counterfactual framework, causal graphs, and causal effect estimation in the point treatment setting using regression, propensity score matching, latent variable models, instrumental variables, and regression discontinuity. A good reference for many important approaches to effect estimation not covered in this course.

  • Tsiatis. Semiparametric Theory and Missing Data. 2006.

  • van der Laan and Robins. Unified methods for censored longitudinal data and causality. 2003

    • Framework for efficient estimation of casual effects using complex longitudinal data in semi-parametric models- emphasis on double robust estimators defined as solutions to estimating equations.

 

Part I: From causal questions to the statistical estimation problem   

Key References:

  • Chapter 2 of Targeted Learning (van der Laan & Rose)

  • Greenland and Pearl. “Causal Diagrams” in S. Boslaugh, editor, Encyclopedia of Epidemiology. 2007​.

  • Pearl. “An Introduction to Causal Inference” Int J Biostat, 6(2):  Article 7, 2010.

  • Pearl. “The Causal Foundations of Structural Equation Modeling” in R. Hoyle, editor, Handbook of Structural Modeling. 2012​

Additional References:

  • Greenland, Pearl and Robins. “Causal Diagrams for Epidemiologic Research” Epidemiology, 10(1): 37-48, 1999.

    • General reference for causal graphs and identifiability results in point treatment setting.

  • Pearl. Causality. Models, Reasoning, and Inference. 2000, 2nd Ed 2009.

    • Chapters 1, 3 & 4.1-4.4 recommended as more formal background on graphs and identifiability.

    • Chapter 7 on link to potential outcome framework.

  • Pearl. “Causal inference in statistics: An overview" Statistics Surveys, 3:96-146, 2009.

Part II : Statistical estimation and interpretation    

Key Readings:

  • Chapters 1, 3,4-6, 9 and 10 of Targeted Learning (van der Laan & Rose)

  • Polley and van der Laan. “Super Learner in Prediction” Technical Report 266, Division of Biostatistics, University of California, Berkeley, 2010. http://www.bepress.com/ucbbiostat/paper266/

  • Petersen, et al. “Diagnosing and responding to violations in the positivity assumption” Statistical Methods in Medical Research, 21(1):31-54, 2012

  • Hernan and Robins. “Estimating causal effects from epidemiological data” J. Epidem and Community Health, 60(7): 578-586, 2006

    • IPTW and G comp in point treatment setting

  • Neugebauer and van der Laan. “Nonparametric causal effects based on marginal structural models” Journal of Statistical Planning and Inference, 137(2): 419-434, 2007.

    • More formal reference for the concept of defining the target parameter using a marginal structural working model.

Additional References:

  • Bembom et. al., "Biomarker Discovery Using Targeted Maximum Likelihood Estimation: Application to the Treatment of Antiretroviral Resistant HIV Infection". Stat Med. 28(1):152-72, 2009.

  • Freedman. Chapter 7. Maximum Likelihood. Statistical Models. 2009. 

  • Freedman. Chapter 8. The Bootstrap. Statistical Models. 2009. 

  • Hampel. “The influence curve and its role in robust estimation” JASA, 69(346): 383-393, 1974.

  • Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Data mining, Inference and Prediction. 2009.

    • Reference on data adaptive estimation, cross validation, machine learning algorithms

  • Petersen et al. “Assessing the effectiveness of antiretroviral adherence interventions” JAIDS, 43(Suppl 1):S96-S103, 2006.

    • Informal overview of three classes of estimators in point treatment setting​

  • Rosenblum and van der Laan. "Targeted Maximum Likelihood Estimation of the Parameter of a Marginal Structural Model," The International Journal of Biostatistics, 6(2):Article 19, 2010. 

  • van der Laan and Dudoit. "Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples". Technical Report 266, Division of Biostatistics, University of California, Berkeley, 2003. www.bepress.com/ucbbiostat/paper130

    • Formal Loss-based learning reference

  • van der Laan, Polley, and Hubbard. “Super learner” Statistical Applications in Genetics  and Molecular Biology, 6(25):Article 25, 2007.

    • Formal SL reference

  • van der Laan and Rubin. "Targeted Maximum Likelihood Learning" The International Journal of Biostatistics, 2(1):Article 11, 2006.

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Suggested citation for the course: 

M. Petersen and L. Balzer. Introduction to Causal Inference. UC Berkeley, August 2014. <www.ucbbiostat.com>