Lecture Schedule

Jan 7

Lecture Introduction: [PPTX]; [PDF]

Reading Materials

Jan 9

Lecture 1 Causality via Experiments: [PPTX]; [PDF]; [Demo Code]

Reading Materials

Chapter 2 of Textbook

Coding Materials

Further reading

Jan 13

Homework HW1 Released

Jan 14

Lecture Inference in linear models: [PPTX]; [PDF]; [Demo Code]

Basics of statistical inference in linear models; confidence intervals for p « n; interpretation of coefficient as partialling out; inference on ATE from trials via regression; Revisiting the role of covariates in randomized trials: precision and heterogeneity: variance characterization and comparisons

Reading Materials

Chapter 1 of Textbook

Coding Materials

Further Reading

Lovell, A Simple Proof of the FWL Theorem
Cattaneo, Jansson, Newey, Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
Kuchibhotla, Brown, Buja, Model-free study of ordinary least squares linear regression

Jan 16

Lecture Inference in linear models: [PPTX]; [PDF]; [Demo Code]; [Demo Code]

Basics of statistical inference in linear models; confidence intervals for p « n; interpretation of coefficient as partialling out; inference on ATE from trials via regression; Revisiting the role of covariates in randomized trials: precision and heterogeneity: variance characterization and comparisons

Reading Materials

Chapter 1 of Textbook

Coding Materials

Further Reading

Lovell, A Simple Proof of the FWL Theorem
Cattaneo, Jansson, Newey, Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
Kuchibhotla, Brown, Buja, Model-free study of ordinary least squares linear regression

Jan 20

Homework HW2 Released

Jan 21

Homework HW1 Due

Lecture Prediction with high dimensional linear models: [PPTX]; [PDF]; [Demo Code]

High dimensional methods and prediction; regularization; lasso; elasticnet;

Reading Materials

Chapter 3 of Textbook

Coding Materials

Jan 23

Lecture Inference in high-dimensional linear models: [PPTX]; [PDF]; [Demo Code]

double lasso; partialling out; intro to Neyman orthogonality; joint inference on multiple parameters

Reading Materials

Chapter 4 of Textbook

Coding Materials

Jan 29

Homework HW3 Released

Jan 28

Homework HW2 Due

Lecture Causality in observational data: [PPTX]; [PDF]

confounding; conditional ignorability; identification by conditioning; identification via propensity scores

Reading Materials

Chapter 5 of Textbook

Further Reading

Jan 30

Lecture Structural Equations Models and DAGs: [PPTX]; [PDF]

language of structural equation models (SEMs); conditional exogeneity; language of interventions and “fixing”; direct and in-direct effects

Reading Materials

Chapter 6 of Textbook

Further Reading

The birth weight “paradox” uncovered?

Feb 3

Homework HW4 Released

Feb 4

Homework HW3 Due

Lecture General DAGs and Counterfactuals: [PPTX]; [PDF]

Single World Intervention Graphs (SWIGs); D-separation; Interventions; Re-visting identification by conditioning;

Reading Materials

Chapter 7 of Textbook

Further Reading

Feb 6

Lecture Valid Adjustments Sets from DAGs: [PPTX]; [PDF]

Single World Intervention Graphs (SWIGs); Graphical criteria for valid adjustment sets; Good and Bad controls

Reading Materials

Chapter 8 of Textbook

Further Reading

Feb 10

Homework HW5 Released

Feb 11

Homework HW4 Due

Lecture Modern Non-Linear Prediction: [PPTX]; [PDF]; [Demo Code]

trees and forests;

Reading Materials

Chapter 9 of Textbook

Coding Materials

Feb 13

Lecture Modern Non-Linear Prediction 2: [PPTX]; [PDF]; [Demo Code]

neural networks; stacking; auto-ml; feature engineering and pre-trained models

Reading Materials

Chapter 11 of Textbook

Coding Materials

Non-Linear ML for Wage Prediction

Further Reading

Feb 18

Lecture Statistical inference with non-linear models: [PPTX]; [PDF]; [Demo Code]

Debiased ML for ATE under partially linear and fully non-linear models; Generic debiased ML framework

Reading Materials

Chapter 10 of Textbook

Coding Materials

DML for Impact of 401(K) Eligibility on Financial Wealth

Feb 20

Lecture Statistical inference with non-linear models 2: [PPTX]; [PDF]; [Demo Code]

Finalizing theory; Examples; segway to unobserved confounding and omitted variable bias;

Reading Materials

Chapter 10 of Textbook

Coding Materials

DML for Impact of 401(K) Eligibility on Financial Wealth

Feb 24

Homework HW6 Released

Feb 25

Homework HW5 Due

Lecture Unobserved Confounding and Instruments: [PPTX]; [PDF]

Omitted Variable Bias; Instrumental variables;

Reading Materials

Chapter 12 of Textbook
Chapter 13 of Textbook

Coding Materials

Further Reading

Further Coding Materials

Git Repo for Omitted Variable Bias with ML

Feb 27

Lecture Heterogeneous Effects and Policy Learning: [PPTX]; [PDF]

Estimation of Conditional Average Treatment Effects (CATE); Best Linear CATE; Inference on Best Linear CATE

Reading Materials

Chapter 14 of Textbook
Chapter 15 of Textbook

Further Reading

Coding Materials

Mar 3

Homework HW7 Released

Mar 4

Homework HW6 Due

Lecture Heterogeneous Effects and Policy Learning: [PPTX]; [PDF]

Estimation of Conditional Average Treatment Effects (CATE); Estimation of CATE from observational data; meta learners;

Reading Materials

Chapter 15 of Textbook

Further Reading

Coding Materials

Mar 6

Lecture Topics on Longitudinal Data and Causal ML: [PPTX]; [PDF]

Difference-in-Differences, Dynamic Treatment Effects

Reading Materials

Chapter 16 of Textbook

Further Reading

Mar 11

Lecture Topics on Longitudinal Data and Causal ML: [PPTX]; [PDF]

Dynamic Treatment Effects, Surrogates

Mar 13

Lecture Discussion and Q&A: [PPTX]; [PDF]

Mar 16

Homework HW7 Due

Lecture Schedule

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10