Lectures in course MGT-416: Causal inference

Explores counterfactuals in SEMs and D-Separation in graphical models.

Learning Latent Models in Graphical Structures

Explores learning latent models in graphical structures, focusing on scenarios with incomplete samples and introducing the notion of distance among variables.

Latent Tree Learning

Explores latent tree learning, covering node properties, sibling relationships, and algorithmic structure.

Pearl's Do Calculus: Rules and Intuition

Explains Pearl's do calculus rules and intuition, including action/observation exchange and interventions.

Causal Inference: Learning Graph Structures

Explores causal inference through learning graph structures for causal reasoning from observational data.

Edge Elimination and Orientation in Graphs

Covers edge elimination and orientation in graphs, including first order tests and logical orientation.

Probability Theory: Joint Marginals and Granger Causality

Covers joint marginals and Granger causality in probability theory, explaining their implications in predicting outcomes.

Approximate Inference Algorithm

Covers the Chow-Liu algorithm for approximate inference and optimal tree approximation.

Ordinary Least Squares Regression Analysis

Covers Ordinary Least Squares Regression analysis and regression model restrictions.

Instrumental Inequality: Binary Variables

Explores instrumental inequality with binary variables and their generation process through arbitrary functions and observed variables.

Gaussian Acyclic Models: Linearity and Identifiability

Covers Gaussian Acyclic Models focusing on linearity and identifiability.

Direct LiNGAM Algorithm

Covers the Direct LiNGAM Algorithm, focusing on causal order estimation and residual minimization.

Causal Inference: Adjustment Sets

Explains adjustment sets in causal inference, emphasizing valid and candidate sets.

Causal Inference: Front Door Criterion

Explains the front door criterion in causal inference and its sufficient conditions for variables blocking paths effectively.

Front Door Criterion: Adjustment Formula

Explores the front door criterion for valid adjustment sets in causal inference.

Higher Education and Successful Marriage

Analyzes the causal effect of higher education on successful marriage and covers criteria for causal inference.