Calendar

Jan 9

Lecture Introduction Causal machine learning in practice

Jan 9

Lecture Potential Outcomes and DAGs

Causal identification via the potential outcomes framework, the structural equation framework and its DAG representation. Single world intervention graphs (SWIGs) and identification by adjustment.

Required Reading Materials

Further Reading

The Central Role of the Propensity Score in Observational Studies for Causal Effects

Jan 16

Lecture DAGs, D-separation and Identification by Conditioning

Proof of D-separation implies conditional independence.

Required Reading Materials

Single World Intervention Graphs [p.1-54]

Further Reading Materials

Jan 16

Lecture Unobserved Confounding beyond DAGs

Causal identification in the presence of unobserved confounding beyond DAGs. Front-Door, Instrumental variables (IV), Diff-in-Diff (DiD), Regression Discontinuity Design (RDD).

Required Reading Materials

Further Reading Materials

Jan 23

Lecture Background: Convergence in Probability, Concentration of Measure and Central Limit Theorem, Parametric Inference

Jan 30

Project Proposal Due

Jan 30

Lecture Semi-Parametric Inference and Neyman Orthogonality

Estimation via moment conditions. Neyman orthogonality and Debiased Machine Learning. Proof of asymptotic linearity for Neyman orthogonal moments with sample splitting and without sample splitting. Automatic Debiased Machine Learning and proof. Proof of the Lasso rate. Discuss the multiplier bootstrap for joint inference.

Required Reading Materials

Further Reading Materials

Feb 6

Lecture Identification and Inference in Dynamic Regimes

Identification and inference of dynamic counterfactual policies (non-parametric and semi-parametric). Estimation of optimal dynamic regimes and g-estimation. Identification proof. Auto-DML and proof for dynamic regime

Required Reading Materials

Further Reading Materials

Feb 13

Lecture Identification and Inference with Proxies and Instruments

Identification and inference of average treatment effects with unobserved confounding when we have access to either proxy controls or instrumental varialbes

Required Reading Materials

An Introduction to Proximal Causal Learning

Further Reading Materials

Feb 20

Lecture Background: Concentration Inequalities and Rademacher Complexity

Feb 20

Lecture Statistical Learning Theory

Rademacher complexity and Localized rademacher complexities and generalization bounds.

Feb 27

Lecture Statistical Learning Theory for Heterogeneous Effects

Orthogonal statistical learning theory. Localized rademacher complexities and generalization bounds.

Required Reading Materials

Further Reading Materials

Feb 28

Project Literature Review Due

Mar 6

Lecture Background: U-Statistics and Concentration

Mar 6

Lecture Non-Parametric Confidence Intervals with Random Forests

Non-parametric confidence intervals, random forests and nearest neighbors. Proof of asymptotic linearity for kernel based moment estimators. Proof of bias for k-NN and (maybe proof of bias for Trees). Proof of confidence intervals with nuisance parameters and local orthogonality.

Required Reading Materials

Further Reading Materials

Mar 13

Lecture Student Presentations

Mar 20

Project Final Report Due

Calendar

Causal Identification

Semi-Parametric Inference and Causal Estimation

Estimation and Inference on Causal Functions