Applied Causality
Spring 2019
Columbia University

• Description
• Instructor: David M. Blei (office hours)
• Meeting: Monday 2:10PM-4:00PM, NWC 602
• Piazza site
• LaTeX template

Syllabus and readings

Here are some of the topics we will cover in class. (We will not necessarily cover all these topics, or in this order.)

• Introduction
• Causal graphs
  • Hernan and Robins (2019), Chapter 6
  • Morgan and Winship (2015), Chapter 3
  • Pearl (2009), Chapter 1.3, 1.4, 1.5
  • Pearl (2009a), Sections 2, 4
  • Peters et al. (2018), Chapters 1, 2, 3
• Adjustment
  • Hernan and Robins (2019), Chapter 7
  • Morgan and Winship (2015), Chapter 4.1, 4.2, 4.6
  • Pearl (2009), Chapter 3.1, 3.2, 3.3
  • Shalizi (2019), Chapter 21, 22
• Selection bias
  • Hernan and Robins (2019), Chapter 8
  • Bareinboim et al. (2014)
• Instrumental variables and related ideas
  • Hernan and Robins (2006), Chapter 16
  • Morgan and Winship (2015), Chapter 9
  • Swanson et al. (2018)
  • Hernan and Robins (2019), Chapter 16
  • Sharma et al. (2016)
• Estimating causal effects
  • Shalizi (2019), Chapter 24
  • Hernan and Robins (2019), Chapters 11-15
• Potential outcomes
  • Morgan and Winship (2015), Chapter 2
  • Imbens and Rubin (2015), Chapter 1
• Bayesian inference, potential outcomes, and randomization
  • Imbens and Rubin (2015), Chapter 8
  • Rubin (1978)
• Counterfactuals
  • Pearl (2009), Chapter 7
  • Pearl et al. (2016), Chapter 4
  • Peters et al. (2018), Chapter 6
  • Zhang and Bareinboim (2018)
• Criticizing causal inference
  • Freedman (2009), Chapter 15
• Causal inference and philosophy
  • Fitelson and Hitchcock (2011)
  • Gelman and Shalizi (2012)
  • Sober (2015)
• Proxy variables
  • Kuroki and Pearl (2014)
  • Miao et al. (2018)
• Sensitivity analysis and confounding
  • Greenland (1996)
  • Robins et al. (2000)
• Multiple causality
  • Song et al. (2015)
  • Ranganath and Perotte (2018)
  • Tran and Blei (2018)
  • Wang and Blei (2018)
  • D'Amour (2019)
  • Bica et al. (2019)
• Information theory and causality
  • Raginsky (2011)
• Case-control studies and synthetic controls
  • Schuemie et al. (2018)
  • Rothman et al. (2008)
  • Abadie et al. (2010)
  • Doudchenko and Imbens (2017)

References

• A. Abadie, A. Diamond, and J. Hainmueller. Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490):493-505, 2010.
• E. Bareinboim, J. Tian, and J. Pearl. Recovering from selection bias in causal and statistical inference. In AAAI Conference on Artificial Intelligence, 2014. [pdf]
• I. Bica, A. Alaa, and M. van der Schaar. Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders. arXiv:1902.00450, 2019.
• A. D'Amour. On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives. In Artificial Intelligence and Statistics, 2019.
• N. Doudchenko and G. Imbens. Balancing, regression, difference-in-differences and synthetic control methods: A synthesis. arXiv:1610.07748, 2017.
• B. Fitelson and C. Hitchcock. Probabilistic measures of causal strength. In P. M. Illari, F. Russo, and J. Williamson, editors, Causality in the Sciences. Oxford University Press, 2011. [pdf]
• D. Freedman. Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press, 2009.
• A. Gelman and C. Shalizi. Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66:8-38, 2012.
• S. Greenland. Basic methods for sensitivity analysis of biases. International Journal of Epidemiology, 25(6):1107-1116, 1996.
• M. Hernan and J. Robins. Causal Inference. Chapman & Hall/CRC (forthcoming), 2019.
• G. Imbens and D. Rubin. Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction. Cambridge University Press, 2015.
• M. Kuroki and J. Pearl. Measurement bias and effect restoration in causal inference. Biometrika, 101(2):423-437, 2014.
• W. Miao, Z. Geng, and E. J. Tchetgen Tchetgen. Identifying causal effects with proxy variables of an unmeasured confounder. Biometrika, 105(4):987-993, 2018.
• S. Morgan and C. Winship. Counterfactuals and Causal Inference. Cambridge University Press, 2nd edition, 2015.
• J. Pearl. Causality. Cambridge University Press, 2nd edition, 2009.
• J. Pearl. Causal inference in statistics: An overview. Statistical Surveys, 2009.
• J. Pearl, M. Glymour, and N. P. Jewell. Causal Inference in Statistics: A Primer. John Wiley & Sons, 2016.
• J. Peters, D. Janzing, and B. Scholkopf. Elements of Causal Inference : Foundations and Learning Algorithms. MIT Press, 2018.
• R. Ranganath and A. Perotte. Multiple causal inference with latent confounding. arXiv:1805.08273, 2018.
• M. Raginsky. Directed information and Pearl's causal calculus. arXiv:1110.0718v1, 2011.
• J. Robins, A. Rotnitzky, and D. O. Scharfstein. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In M. E. Halloran and D. Berry, editors, Statistical Models in Epidemiology, the Environment, and Clinical Trials. Springer, 2000.
• K. Rothman, S. Greenland, and T. Lash. Case-Control Studies. Encyclopedia of Quantitative Risk Analysis and Assessment. American Cancer Society, 2008.
• D. Rubin. Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1):34-58, 1978.
• M. Schuemie, G. Hripcsak, P. Ryan, D. Madigan, and M. Suchard. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proceedings of the National Academy of Sciences, 115(11):2571-2577, 2018
• C. Shalizi. Advanced Data Analysis from an Elementary Point of View. In preparation, 2019.
• A. Sharma, J. Hofman, and D. Watts. Split-door criterion: Identification of causal effects through auxiliary outcomes. Annals of Applied Statistics, to appear.
• E. Sober. Ockham's Razors : A User's Manual. Cambridge University Press, 2015.
• M. Song, W. Hao, and J. Storey. Testing for genetic association in arbitrarily structured populations. Nature Genetics, 2015.
• S. Swanson, M. Hernan, M. Miller, J. Robins, and T. Richardson. Partial identification of the average treatment effect using instrumental variables: Review of methods for binary instruments, treatments, and outcomes. Journal of the American Statistical Association, 113(522):933-947, 2018.
• D. Tran and D. Blei. Implicit causal models for genome-wide association studies. In International Conference on Learning Representations, 2018.
• Y. Wang and D. Blei. The blessings of multiple causes. arXiv:1805.06826, 2018.
• J. Zhang and E. Bareinboim. Fairness in decision-making-the causal explanation formula. AAAI Conference on Artificial Intelligence, 2018.