Course: Causal Inference and Discovery (DS3223/DS6273/MT3273/MT6433)

Lecture hall: LHC 301 (NKN classroom);

Virtual link: Google meet

Instructor: Tulasi Ram Reddy (tulasimath ‘at’ gmail ‘dot’ com; Room No: 406)

TA: Mihir Hasabnis (mihirhasabanis ‘at’ gmail ‘dot’ com; Room No: )

Office hours:

Correlation

Topics to be covered: Simpson’s paradox, Structural Causal Model (SCM), Design of Experiments, Potential Outcomes Framework, Causal Graphical Model, d-separation, do-operator, Rubin’s G-formula, Causal Discovery, If time permits: Regression Discontinuity Designs, Difference in Differences, Instrumental Variables Estimation.

Prerequisites: Familiarity with basic statistical concepts such as estimation techniques, hypothesis testing, and probability distributions. Exposure to regression analysis, including simple linear regression and multiple regression, is essential. Understanding of Linear algebra and multivariate calculus (derivatives and integrals) (derivatives and integrals) is necessary. Further knowledge of graphs and properties is useful.

Lecture Date Contents Supplementary material
1 1-January Introduction John Snow and 1854 Cholera outbreak
2 11-January Simpson’s Paradox See Chapter-1 from Peng Ding’s lecture notes
3 13-January Review Properties of Gaussian random variables (From a lecture notes by Manjunath Krishnapur )
4 13-January Partial regression Class test-I on 16-January-2024
5 15-January Treatments, effects and confounders  
6 18-January Potential outcomes framework (Neyman/Rubin’s causal model)  
7 20-January Causal Graphical Model Class test-II on 1-February-2024
8 25-January d-Separation  
9 29-January Structural Causal Model For SCM and related topics you may see Chapters-3 and 6 from the book Elements of Causal Inference: Foundations and Learning Algorithms by J. Peters, D. Janzing and B. Schölkopf
10 30-January Robin’s G-formula/Trucated Factorization Check DAGitty
11 6-February Backdoor criteria Class test-III on 15-February-2024
12 10-February Adjustment formula For topics until Mid-Sem, you may refer Chapters 1, 2 and 3 from the book Causal Inference in Statistics by J. Pearl, M. Glymour, N. P. Jewell
13 12-February Front door criteria  
14 13-February Matching  
  23-February Mid-Semester Exam  
15 27-February Propensity score Python packages: DoWhy, EconML, causal-learn
16 4-March IPW  
17 11-March Makeup Lecture  
18 18-March Instrumental Variables-1 Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity by Guido W. Imbens and Joshua D. Angrist
19 19-March Instrumental Variables -2 Identification and Estimation of Local Average Treatment Effects by Guido W. Imbens and Joshua D. Angrist
20 26-March Instrumental Variables-3  
21 30-March Regression Discontinuity Designs - 1  
22 30-March Regression Discontinuity Designs - 2  
23 1-April Difference in Differences (DID) -1 Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania by David Card and Alan B. Krueger
24 2-April Difference in Differences (DID) -2 For topics after Mid-Sem, you may refer Chapters 5, 6, 7, 9 in Causal Inference: The mixtape by Scott Cunningham
25 8-April Causal Discovery (PC Algorithm) Introduction to the foundations of causal discovery
26 13-April Independence tests, Algorithms(FCI/GES/LiNGAM) Causal Discovery Methods Based on Graphical Models
  29-April End-Semester Exam Due date for class projects submission
Problem Sets: Set-1 Set-2 Set-3

References: