From a5984c276cc4004565644aff469163de80beb40f Mon Sep 17 00:00:00 2001 From: hlan-98 <61669187+hlan-98@users.noreply.github.com> Date: Mon, 29 Jan 2024 17:31:47 -0800 Subject: [PATCH] Update week-02.md --- _modules/week-02.md | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/_modules/week-02.md b/_modules/week-02.md index 0c8f585..b4dba3a 100644 --- a/_modules/week-02.md +++ b/_modules/week-02.md @@ -22,18 +22,13 @@ Jan 17 - Kuchibhotla, Brown, Buja, [Model-free study of ordinary least squares linear regression](https://arxiv.org/pdf/1809.10538.pdf) Jan 19 -: **Lecture**{: .label .label-green } Inference in linear models - : [[PPTX]](https://github.com/stanford-msande228/winter23/raw/main/MSANDE228_Lecture3_Inference_in_Linear_Models.pptx) - : [[PDF]](https://github.com/stanford-msande228/winter23/raw/main/MSANDE228_Lecture3_Inference_in_Linear_Models.pdf) - : [[Demo Code]](https://github.com/stanford-msande228/winter23/blob/main/Lecture2-Demo.ipynb) -: 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 +: **Lecture**{: .label .label-green } Prediction in high dimensional linear models + : [[PPTX]](https://github.com/stanford-msande228/winter23/raw/main/MSANDE228_Lecture4_Inference_in_High_Dimensional_Linear_Models.pptx) + : [[PDF]](https://github.com/stanford-msande228/winter23/raw/main/MSANDE228_Lecture4_Inference_in_High_Dimensional_Linear_Models.pdf) + : [[Demo Code]](https://github.com/stanford-msande228/winter23/blob/main/Lecture3-Demo.ipynb) +: High dimensional methods and prediction; regularization; lasso; elasticnet; : ***Reading Materials*** -- Chapter 1 of [Textbook](https://canvas.stanford.edu/courses/168439/files/folder/Readings) +- Chapter 3 of [Textbook](https://canvas.stanford.edu/courses/168439/files/folder/Readings) : ***Coding Materials*** -- [Predicting Wages](https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/PM1_prediction.ipynb) -- [Predictive Inference on Wage Gap](https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/inference.ipynb) -: ***Further Reading*** -- Lovell, [A Simple Proof of the FWL Theorem](https://www.jstor.org/stable/41426805) -- Cattaneo, Jansson, Newey, [Inference in Linear Regression Models with Many Covariates and Heteroscedasticity](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1328360) -- Kuchibhotla, Brown, Buja, [Model-free study of ordinary least squares linear regression](https://arxiv.org/pdf/1809.10538.pdf) - +- [Penalized Linear Regressions: Simulated Data](https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/PM2/linear-penalized-regs.ipynb) +- [Predicting Wages with Penalized Regressions](https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/PM2/ml-for-wage-prediction.ipynb)