Coding Example
Coding Example
Causal Inference with Linear Regression: A Modern Approach (Part I)
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A Crash Course on The Basics of Causal Inference
Introduction
Slides
How to Use This Course
Course Guidelines
What is Causal Inference and why do we need it?
Individual Treatment Efffect
(Conditional) Average Treatment Effect
The do-operator
Ignorability
Conditional Ignorability & The Adjustment Formula
Causal Graphs
Graph Patterns
Blocking Paths
Backdoor Adjustment
Double-headed arrows & The do-operator in Causal Graphs
Coding Example: Generating Data
Coding Example: Observational & Experimental study (no adjustment)
Coding Example: Observational study (with adjustment)
Extending to Continuous Treatments
Recap
Theoretical Questions
Coding Exercise
Resources
The Mechanics of Linear Regression
Introduction
Slides
Prerequisites
Linear Regression
Closed-Form Solution
OLS Residuals
From Population to Sample
The CEF
CEF Properties
Linear Regression as CEF Approximator
Closed-Form Formula Revisited
FWL & The Regression Anatomy Formula
Changing Slope Coefficients Part I
Changing Slope Coefficients Part II
Recap
Theoretical Questions
Coding Exercise
Resources
When Linear Regression Coefficients Reflect Causal Effects
Introduction
Slides
Recap Causality Basics
Total, Direct & Indirect Effects
Structural Causal Models
Linear SCMs & The do-operator
The Meaning of Structural Parameters Part I
The Meaning of Structural Parameters Part II
Linear Structural Equation vs Linear Regression Equation
Identifying Structural Coefficients for All Variables Part I
Identifying Structural Coefficients for All Variables Part II
Coding Example: Independent Structural Errors
Coding Example: Dependent Structural Errors
Linear Regression: From Searching for The ‘"True Model’ to Control Tool
The Single-Door Criterion
Coding Example: Single-Door Criterion
Total Effects In Terms of Structural Parameters
The Back-Door Criterion
Coding Example: Back-Door Criterion
Helping Out The Fitness Team
On The Nuisance of Control Variables
Econometrics & Causality
Recap
Theoretical Questions
DAGitty
Coding Exercise
Resources
Robustness Tests & Sensitivity Analysis
Introduction
Slides
What Are Robustness Tests?
Detecting Omitted Variable Bias Part I
Coding Example
Detecting Omitted Variable Bias Part II
Coding Example
What Is Sensitivity Analysis?
The Real-World Example
The Traditional OVB Framework
Applying The Traditional OVB Formula
(Partial) R-Squared
A Partial R-Squared Reparametrization of the OVB Formula
Applying The Reparametrized OVB Formula
Bounding The Strength of An Omitted Confounder
Applying The Bounding Procedure
Extreme Scenario Analysis
Applying Extreme Scenario Analysis
Summary Metrics
Applying Summary Metrics
Extending With Inference & Multiple Omitted Confounders
The Package: PySensemakr
Recap
Theoretical Questions
Coding Exercise
Resources
Part 2 & Recommendations
Part 2 & Recommendations
Reading List & Sources
Feedback and Reviews
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Causal Inference with Linear Regression: A Modern Approach (Part I)
Buy now
Learn more
A Crash Course on The Basics of Causal Inference
Introduction
Slides
How to Use This Course
Course Guidelines
What is Causal Inference and why do we need it?
Individual Treatment Efffect
(Conditional) Average Treatment Effect
The do-operator
Ignorability
Conditional Ignorability & The Adjustment Formula
Causal Graphs
Graph Patterns
Blocking Paths
Backdoor Adjustment
Double-headed arrows & The do-operator in Causal Graphs
Coding Example: Generating Data
Coding Example: Observational & Experimental study (no adjustment)
Coding Example: Observational study (with adjustment)
Extending to Continuous Treatments
Recap
Theoretical Questions
Coding Exercise
Resources
The Mechanics of Linear Regression
Introduction
Slides
Prerequisites
Linear Regression
Closed-Form Solution
OLS Residuals
From Population to Sample
The CEF
CEF Properties
Linear Regression as CEF Approximator
Closed-Form Formula Revisited
FWL & The Regression Anatomy Formula
Changing Slope Coefficients Part I
Changing Slope Coefficients Part II
Recap
Theoretical Questions
Coding Exercise
Resources
When Linear Regression Coefficients Reflect Causal Effects
Introduction
Slides
Recap Causality Basics
Total, Direct & Indirect Effects
Structural Causal Models
Linear SCMs & The do-operator
The Meaning of Structural Parameters Part I
The Meaning of Structural Parameters Part II
Linear Structural Equation vs Linear Regression Equation
Identifying Structural Coefficients for All Variables Part I
Identifying Structural Coefficients for All Variables Part II
Coding Example: Independent Structural Errors
Coding Example: Dependent Structural Errors
Linear Regression: From Searching for The ‘"True Model’ to Control Tool
The Single-Door Criterion
Coding Example: Single-Door Criterion
Total Effects In Terms of Structural Parameters
The Back-Door Criterion
Coding Example: Back-Door Criterion
Helping Out The Fitness Team
On The Nuisance of Control Variables
Econometrics & Causality
Recap
Theoretical Questions
DAGitty
Coding Exercise
Resources
Robustness Tests & Sensitivity Analysis
Introduction
Slides
What Are Robustness Tests?
Detecting Omitted Variable Bias Part I
Coding Example
Detecting Omitted Variable Bias Part II
Coding Example
What Is Sensitivity Analysis?
The Real-World Example
The Traditional OVB Framework
Applying The Traditional OVB Formula
(Partial) R-Squared
A Partial R-Squared Reparametrization of the OVB Formula
Applying The Reparametrized OVB Formula
Bounding The Strength of An Omitted Confounder
Applying The Bounding Procedure
Extreme Scenario Analysis
Applying Extreme Scenario Analysis
Summary Metrics
Applying Summary Metrics
Extending With Inference & Multiple Omitted Confounders
The Package: PySensemakr
Recap
Theoretical Questions
Coding Exercise
Resources
Part 2 & Recommendations
Part 2 & Recommendations
Reading List & Sources
Feedback and Reviews