Applied Causality

Spring 2017, Columbia University

David M. Blei

Day/Time: Wednesdays, 2:10PM - 4:00PM
Location: 302 Fayerweather

Course Description

We will study applied causality, especially as it relates to Bayesian modeling. Topics include probabilistic graphical models, potential outcomes, posterior predictive checks, and approximate posterior inference. Each student will embark on a semester-long project around applied causal inference.

Reading assignments

Introduction and logistics
Potential outcomes

S. Morgan and C. Winship. Counterfactuals and Causal Inference. Cambridge University Press, 2nd edition, 2015; Chapter 2.
G. Imbens and D. Rubin. Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction. Cambridge University Press, 2015; Chapter 1. (optional)

Causal graphs

S. Morgan and C. Winship. Counterfactuals and Causal Inference. Cambridge University Press, 2nd edition, 2015; Chapter 3.
J. Pearl. Causal inference in statistics: An overview. Statistical Surveys, 2009.

Causal graphs

S. Morgan and C. Winship. Counterfactuals and Causal Inference. Cambridge University Press, 2nd edition, 2015; Chapter 4.

Causal graphs and estimation

J. Pearl. Causality: Models, Reasoning, and Inference, 2nd Edition, Cambridge University Press, 2009; Chapter 3.1, 3.2, 3.3.
C. Shalizi. Advanced Data Analysis from an Elementary Point of View, 2017; Chapter 24 (except 24.2)

Bayesian inference, potential outcomes, and randomization

G. Imbens and D. Rubin. Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction. Cambridge University Press, 2015; Chapter 8.
D. Rubin. Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1):34–58, 1978; (optional).

Bayesian inference, potential outcomes, and observational data

G. Imbens and D. Rubin. Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction. Cambridge University Press, 2015; Chapter 12.
A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari, and D. Rubin. Bayesian Data Analysis. Chapman & Hall/CRC, 3rd edition, 2014; Chapter 8 (optional).

Double robustness

G. Imbens and D. Rubin. Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction. Cambridge University Press, 2015; Chapter 18.

Instrumental variables

S. Morgan and C. Winship. Counterfactuals and Causal Inference. Cambridge University Press, 2nd edition, 2015; Chapter 9.

Counterfactuals

J. Pearl. Causality: Models, Reasoning, and Inference, 2nd Edition, Cambridge University Press, 2009; Chapter 7

Genetic association

M. Song, W. Hao, and J. Storey. Testing for genetic association in arbitrarily structured populations. Nature Genetics, 2015. [Supplement]

Special Guest: Andrew Gelman

A. Gelman. Causality and statistical learning. American Journal of Sociology, 117(3):955–966, 2011.
A. Gelman. Experimental reasoning in social science. In Field Experiments and their Critics. Yale University Press, 2010.

Causality and medicine

P. Schulam and S. Saria. What-if reasoning with counterfactual Gaussian processes. arXiv 1703.10651, 2017.