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  • Designing Experiments Toward Shrinkage Estimation

    by Evan Rosenman and Luke Miratrix
    on May 15, 2024 · 13 min read · MLM multisite visualizations coding  ·
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    Designing Experiments Toward Shrinkage Estimation

    Estimating subgroup impacts in an RCT can be hard. An RCT by itself is usually underpowered for this task–we barely have enough data to give us an overall average, and as subgroups are smaller, they are noisier! One idea recently gaining increased traction is to augment an RCT with observational data. We might use …

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  • Plotting distributions of site-level impact estimates (or other collections of noisily estimated things)

    by Luke Miratrix
    on Apr 23, 2024 · 19 min read · MLM multisite visualizations coding  ·
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    Plotting distributions of site-level impact estimates (or other collections of noisily estimated things)

    Do you ever want to visualize the distribution of effects across sites in a multi-site evaluation (or meta analysis)? For example, consider a multisite trial with 30 sites, where each site is effectively a small randomized experiment. A researcher might fit a multilevel model with a random effect for the impact in each …

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A blog about Causality, Applications, and Research in Education and Statistics.

From the C.A.R.E.S. Lab at the Harvard Graduate School of Education
Director: Luke Miratrix, Associate Professor

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Featured Posts

  • Comparing ATE estimators in multisite and cluster randomized trials
  • Fine-Tuning ChatGPT for Essay Grading
  • The Art of Crafting Prompts for Essay Grading with ChatGPT
  • To block or not to block, that is the question
  • Drawing a Line Between Sample Statistics and Population Inferences
  • So, you decided to write your article in R Markdown

Recent Posts

  • Comparing ATE estimators in multisite and cluster randomized trials
  • Fine-Tuning ChatGPT for Essay Grading
  • The Art of Crafting Prompts for Essay Grading with ChatGPT
  • How to Grade Essays with ChatGPT
  • Designing Experiments Toward Shrinkage Estimation
  • Plotting distributions of site-level impact estimates (or other collections of noisily estimated things)
  • Exploring power with the PUMP package
  • Recovering Effect Sizes from Dichotomous Variables Using Logistic Regression

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