CompSci 590.01 - Causal Inference in Data Analysis >with Applications to Fairness and Explanations (original) (raw)

Reading

Books

Papers

Please see the Google Doc - link shared on Ed

Schedule

(mix of lectures and paper presentations, the first class on Jan 17 will be an introduction to causal inference, overview, and logistics information)

Slides will be uploaded after the lectures/presentations to keep the class interactive and brainstorm together

| | Day | Topic | Slides | Reading | Comments/Optional Reading | | ------ | --------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | | 1 | 1/12 (Th) | No class | | | | 2 | 1/17 (T) | Overview and intro to Causal Inference | Lecture-1 | | | 3 | 1/19 (Th) | Simpson's Paradox, d-Separation, Structural/Graphical Causal Models | Lecture-2 | [Primer] Ch 1, Ch 2.1-2.4 | | 4 | 1/24 (T) | Intervention - Adjustment formula, backdoor criterion | Lecture-3 | [Primer] Ch 3 | | 5 | 1/26 (Th) | Counterfactuals | Lecture-4 | [Primer] Ch 4 | | 6 | 1/31 (T) | Rubin's potential outcome framework & statistical causal inference methods | Lecture-5 | | | 7 | 2/2 (Th) | Intro to fairness & causality for fairness | Lecture-6 | | | 8 | 2/7 (T) | Contd. | | | | 9 | 2/9 (Th) | Intro to explanations & causality for explanations | Lecture-7 | | | 10 | 2/14 (T) | Causality for time-series data | Presentation-1 Nathan and Gaurav | | | 11 | 2/16 (Th) | Almost Matching Exactly for causal inference | Presentation-2 Yiyang and Zhehan | | | 12 | 2/21 (T) | Instrumental variables in causal inference | Presentation-3 Sakina and Shota | Angrist & Imbens (1995) and Syrgkanis et al. (NeurIPS 2019) | | 13 | 2/23 (Th) | GNN and GNN Explainer | Presentation-4 Hanze and Jinze | Ying et al. (NeurIPS 2019) | | 14 | 2/28 (T) | Do-calculus, data fusion, and Transportability for visual recognition | Presentation-5Frankie and Zach | Bareinboim and Pearl (PNAS 2016) and Mao et al. (CVPR 2022) | | 15 | 3/2 (Th) | Interpretability vs. Explainability | Presentation-6Hayoung and Ryan | Rudin (Nature Machine Intelligence 2019) and Zhao and Hastie (2018) | | 16 | 3/7 (T) | Counterfactual explanations | Presentation-7 Ghazal and Srikar | Slack et al. (NeurIPS 2021) and Mothilal et al. (Fat* 2020) | | 17 | 3/9 (Th) | Causal inference for relational data & review of topics so far | Lecture-8 Sudeepa | Salimi et al. (SIGMOD 2020) and Galhotra et al. (SIGMOD 2022) | | 18 | 3/14 (T) | No class - Spring break | | | | 19 | 3/16 (Th) | No class - Spring break | | | | 20 | 3/21 (T) | Study of bias in applications & Counterfactual Fairness | Presentation-8 Jinyi and Yu | Obermeyer et al., (Science 2019) and Kusner et al., (NeurIPS 2017) | | 21 | 3/23 (Th) | Fairnessin database research - Ranking and Selection | Presentation-9 Fangzhu and Yuxi | Shetiya et al. (ICDE 2022) and Asudeh et al., (SIGMOD 2019) | | 22 | 3/28 (T) | Explainable ML classifiers (LIME and ANCHOR) | Presentation-10 Jason and Keyu | Ribeiro et al. (KDD 2016) and (AAAI 2018) | | 23 | 3/30 (Th) | Explainable ML classifiers (SHAP) and Adversarial Attack | Presentation-11 Lecture-9 Theo and Sudeepa | Lundberg and Lee (NeurIPS 2017) and Slack et al. (AIES 2020) | | 24 | 4/4 (T) | Matching and Scalable Matching for Causal Inference with Continuous Covariates | Lecture-10 Guest lecture by Harsh Parikh | Parikh et al. (JMLR 2023)Whiteboard lecture | | 25 | 4/6 (Th) | Auditing and Validating Causal Inference Methods | Lecture-11 Guest lecture by Harsh Parikh | Parikh et al. (ICML 2023) | | 26 | 4/11 (T) | Fairness and Proxy variables | Presentation-12 Kiki and Tamara | Chen et al. (FAT* 2019) and Galhotra et al. (Entropy 2021) | | 27 | 4/13 (Th) | Project presentations | | | | 28 | 4/18 (T) | Project presentations | | |