Advanced Machine Learning

This class was fully concentrated on causality and causal modeling. The class was instructed by Professor Lise Getoor. Following is the reading list for this class.


Graphical Models, slides (pdf)

Reading:  Graphical Models in a Nutshell

Pearl’s Turing Award lecture on Causality, video

Causal Modeling in Statistics

Reading: Statisistics and Causal Inference (here is a local link, in case you have trouble accessing the pdf through the journal site)

Potential Outcomes Models

Reading: Causal Inference Using Potential Outcomes  (Video)

Presenter: Pardis Miri

Propensity Score Matching

Reading: The Central Role of the Propensity Score in Observational Studies for Causal Effects

Structural Equation Models

Reading: Wikipedia entry + Selection from Morgan & Winship, Counterfactuals and Causal Inference (ch3), pdf 

Reading: Selection from Morgan & Winship, Counterfactuals and Causal Inference (ch4), pdf

Causal Modeling: Pearl

Reading: Causal Inference from Big Data: Theoretical Foundations and the Data Fusion Problem, Barienboim and Pearl,

NAS 2015,  pdf

Causal Models: Sprites

Reading: Spirtes, Introduction to Causal Inference (skim, focus on section 4) pdf 

Reading: LoCI algorithm (Logical Conditional independence) UAI 2011 (A Logical Characterization of Constraint-Based Causal Discovery)

Causal Models for Relational Data 

Reading: C. Shalizi and A. Thomas. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40, 2011.    pdf

Causal Relational Models

Reading: B. Sinclair, M. McConnell, and D. Green. Detecting spillover effects: Design and analysis of multilevel experiments. American Journal of Political Science, 56(4), 2012.  pdf

Causal Relational Models

Reading: M. E. Maier, B. J. Taylor, H. Oktay, and D. Jensen. Learning causal models of relational domains. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, (AAAI), 2010. pdf

Optional Reading:

  • M. J. Rattigan, M. E. Maier, and D. Jensen. Relational blocking for causal discovery. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (AAAI), 2011.  pdf

  • A Sound and Complete Algorithm for Learning Causal Models from Relational Data,

Causality in Social Networks:

From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks, Ya Xu, Nanyu Chen, Adrian Fernandez, Omar Sinno, Anmol Bhasin, KDD 2015.  pdf (from acm digital libraries); local copy pdf

P. Toulis and E. K. Kao. Estimation of causal peer influence effects. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013. 

Causal Models for Recommender Systems

Reading: Estimating the Causal Impact of Recommendation Systems from Observational Data, Amit Sharma, Jake M. Hofman and Duncan J. Watts, EC ’15 Proceedings of the Sixteenth ACM Conference on Economics and Computation.  pdf  (alternate pdf link)

Causal Models in NLP

Minimally Supervised Event Causality Identification, Quang Xuan Do Yee Seng Chan Dan Roth, EMNLP 2011.   pdf
Helpful notes: pdf