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 http://arxiv.org/pdf/1202.3711.pdf (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:
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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
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A Sound and Complete Algorithm for Learning Causal Models from Relational Data, http://auai.org/uai2013/prints/papers/197.pdf
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.
http://jmlr.org/proceedings/papers/v28/toulis13.pdf
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