Causal Inference with Linear Regression: A Modern Approach

A Pearlian Approach to Causal Inference with Linear Regression

What you'll learn
- How to estimate total & direct causal effects using Linear Regression
- When Linear Regression coeffcients can or can't be interpreted causally
- The meaning of Linear Structural Causal Models and its parameters
- The difference between Linear Structural Equations and Linear Regression Equations
- How to perform Robustness Tests & Sensitivity Analysis on your OLS estimated causal effect
- How to work with Causal Graphs, Graph Criteria and DAGitty

Requirements
- Basic knowledge of Probability Theory, Linear Algebra & Statistics (Python is beneficial)

Description
Linear regression is one of the most widely used models in the data industry—but also one of the most misunderstood when it comes to Causal Inference.

Too often, its coefficients are wrongly interpreted as causal effects.Traditional assumptions like exogeneity are often emphasized, yet their true meaning is rarely understood. Many can recite the classic OLS assumptions under which coefficients are "unbiased," but struggle to articulate what they are actually unbiased for.

And this isn’t surprising.Educational sources on Linear Regression are extremely vague, ambiguous and often even contradictory when it comes to Causal Inference.

In this 2-part course series, we fill that gap using a fresh and modern approach.You’ll learn exactly how and when Linear Regression coefficients reflect causal effects.

This first part starts by discussing the foundational concepts from Causal Inference that you’ll need to understand to follow the remainder of the course.

In the second module,we’ll go deep into the mechanics of Linear Regression, with an emphasis on how Linear Regression coefficients are computed using Ordinary Least Squares.

In module 3, we introduceLinear Structural Causal models, where you’ll learn that the parameters in these equations are the ones we are actually interested in when estimating causal effects using Linear Regression. We thendiscuss the exact conditions under which Linear Regression coefficients succeed in recovering these true causal parameters

Finally, in module 4, we explorehow well-designed Robustness Tests and Sensitivity Analysis can help you build trust in your causal analysis resultsand better defend your conclusions.

Along the way, we’llclarify some of the most common misconceptions about the causal interpretation of Linear Regression coefficients.

Everything in this course is based on high-quality sources in Causal Inference, including the work of leading researchers like Angrist & Pischke, Carlos Cinelli, Chad Hazlett, and Judea Pearl.

But beyond its strong theoretical foundation, this course is built for real-world application.To reinforce your understanding, we’ll work through numerous coding examples, and each module includes a coding exercise to help you practice with the discussed techniques.

This course is for anyone with a basic understanding of Probability Theory, Linear Algebra, and Statistics. Familiarity with programming is helpful, as coding examples and exercises will be in Python.

So, if you want to stand out and truly understand how to use Linear Regression for Causal Inference correctly, this course is for you!

Who this course is for:
- Everyone who wants to learn how we can use Linear Regression for Causal Inference
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