Category Archives: Causal Inference
Understanding OLS in High-Dimensional Settings: Insights and Practical Implications
In the world of data science and machine learning, linear regression stands as a foundational tool for predictive modeling. Despite its simplicity, its proper implementation, especially in high-dimensional settings, demands a nuanced understanding. This blog post dives into the intricacies of linear regression, focusing on how dimensionality impacts wage gap estimates and the challenges associated…
Detailed Explanation of Partialling-Out and the Frisch-Waugh-Lovell (FWL) Theorem
Partialling-Out Partialling-out is a technique used in regression analysis to isolate the effect of a specific variable (regressor) on the outcome by removing the influence of other variables (control variables). This helps us understand the true relationship between the target regressor and the outcome. Summary
Chapter 1 Summary: Applied Causal Inference Powered by ML and AI
Regression and the Best Linear Prediction Problem Linear regression is a method for predicting a dependent variable (Y) using one or more independent variables (X). Here’s a simplified breakdown: Practical Implications: Best Linear Approximation Property This property indicates that our best linear prediction (β′X) is also the best linear approximation to the conditional expectation of…