MCI: Lecture Slides

Lecture 1: Introduction

Lecture 2: Basics of probability

Lecture 3: Regression and Graphs

Lecture 4: Potential Outcomes

Lecture 5: Potential outcomes, propensity score

Lecture 6: Instrumental variables

Lecture 7: Sensitivity Analysis

Lecture 8: Difference in difference

Lecture 9: Pearl's do operator

Lecture 10: The adjustment formula (Pearl)

Lecture 11: Front door

Lecture 12: PC algorithm

Lecture 13: Do_calculus_optimal_conditioning

Lecture 14: Mediation part 1

Lecture 15: Counterfactual

Lecture_16_17: Counterfactuals, attribution, mediation

Lecture 18: ANMs

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