Jan 9 Lecture Introduction[PPTX] [PDF] Reading Materials Jan 11 Lecture 1 Causality via Experiments[PPTX] [PDF] [Demo Code] Reading Materials Coding Materials Further reading Survey Please complete the background survey by the end of Week 1 Link Jan 16 Homework HW1 ReleasedGoogle Doc 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 comparisonsReading Materials Coding Materials Further Reading Jan 18 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 comparisonsReading Materials Coding Materials Further Reading Jan 23 Lecture Prediction in high dimensional linear models[PPTX] [PDF] High dimensional methods and prediction; regularization; lasso; elasticnet;Reading Materials Coding Materials Jan 25 Lecture Inference in high-dimensional linear models[PPTX] [PDF] [Demo Code] double lasso; partialling out; intro to Neyman orthogonality; joint inference on multiple parametersReading Materials Coding Materials Jan 29 Homework HW2 ReleasedGoogle Doc Jan 30 Lecture Causality in observational data[PPTX] [PDF] confounding; conditional ignorability; identification by conditioning; identification via propensity scoresReading Materials Further Reading Feb 1 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 effectsReading Materials Further Reading Feb 6 Lecture General DAGs and Counterfactuals[PPTX] [PDF] Single World Intervention Graphs (SWIGs); D-separation; Interventions; Re-visting identification by conditioning; Proof of D-separation implies conditional independenceReading Materials Further Reading Feb 8 Homework HW2 Duegradescope Lecture Valid Adjustments Sets from DAGs[PPTX] [PDF] [Demo Py] [Demo R] Single World Intervention Graphs (SWIGs); Graphical criteria for valid adjustment sets; Good and Bad controlsReading Materials Further Reading Feb 13 Lecture Modern Non-Linear Prediction[PPTX] [PDF] [Demo Code] trees and forests;Reading Materials Coding Materials Feb 15 Lecture Modern Non-Linear Prediction 2[PPTX] [PDF] [Demo Code] neural networks; stacking; auto-ml; feature engineering and pre-trained modelsReading Materials Coding Materials Further Reading Feb 20 Lecture Statistical inference with non-linear models[PPTX] [PDF] Debiased ML for ATE under partially linear and fully non-linear models; Generic debiased ML frameworkReading Materials Coding Materials Feb 22 Lecture Statistical inference with non-linear models 2[PPTX] [PDF] Finalizing theory; Examples; segway to unobserved confounding and omitted variable bias;Reading Materials Coding Materials Feb 23 Homework HW3 ReleasedGoogle Doc Feb 27 Lecture Unobserved Confounding and Instruments[PPTX] [PDF] Omitted Variable Bias; Instrumental variables;Reading Materials Coding Materials Further Reading Further Coding Materials Feb 29 Lecture Unobserved Confounding and Instruments[PPTX] [PDF] Local Average Treatment Effect (LATE); Debiased ML inference in partially linear and non-linear IV models; inference with weak instruments; DML with weak identificationReading Materials Coding Materials Mar 5 Lecture Heterogeneous Effects and Policy Learning[PPTX] [PDF] Estimation of Conditional Average Treatment Effects (CATE); Best Linear CATE; Inference on Best Linear CATEFurther Reading Mar 7 Homework HW3 DueLecture Heterogeneous Effects and Policy Learning 2[PPTX] [PDF] [Demo Code] Estimation of CATE from observational data; meta learners;Further Reading Coding Materials Mar 11 Homework HW4 Released[Google Doc] Mar 12 Lecture Heterogeneous Effects and Policy Learning 3[PPTX] [PDF] [Demo Code] neural network methods; Evaluation and model selection of CATE methods; policy learning;Further Reading Coding Materials Mar 14 Lecture Advanced Topics[PPTX] [PDF] [Demo] Further Reading This site uses Just-the-Class (https://github.com/kevinlin1/just-the-class), a class theme for Jekyll, which inherits from Just-the-Docs.