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Beyond DiD: Autoregressive Models for Policy Evaluation When Parallel Trends Fail
causal inference
policy evaluation
methodology
This post demonstrates how autoregressive models can overcome the limitations of difference-in-differences analysis when evaluating health policies with non-parallel pre-treatment trends, providing researchers with practical tools to improve causal inference in observational studies.
DIY Decision Trees
decison trees
machine learning
Embarking on a DIY Journey with Machine Learning Algorithms.
Does it Spark Joy? Using PCA for Dimensionality Reduction in R
PCA
Principal Component Analysis (PCA) is a powerful tool for dimensionality reduction. In this post, we provide an overview of PCA, explaining its purpose and how it works. We then walk through a hands-on application of PCA using R, demonstrating the step-by-step process of PCA and its application in predictive modeling.
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