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Causal Inference with R - Regression

Actualizado 1/2025

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0 minutos23 vídeos58 ejercicios3,500 XP2,696Certificado de logros

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Descripción del curso

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this course focuses on how to use regression to find causal effects, why they can be controversial, and what they look like in practice. We’ll stay away from dense statistical math and focus instead on higher level concepts that data scientists need to always consider when examining and making inferences about data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Brian Aronson (Duke), Dr. Tyler Ransom (University of Oklahoma), and Alexandra Cooper (Duke).

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1

Regressions 1: Introduction to Regression As Causality

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Course Trailer for Causal Inference with R - Regression

Introduction to Regression Analysis

Interpreting Regressions

Reversing Causal Direction

Let’s Code: Comparing Hospital Quality to Mortality Rates

Comparing Hospital Quality to Mortality Rates

Regression Models and Policy Implementation

Basic Elements of a Regression Table

Reading a Bivariate Regression Table

Reading a Multivariate (Multiple) Regression Table

The Relationship between Economic Development and Property Rights

Regressions with small coefficients and small confidence intervals

Recalling terminology

Multiple Regression Models

Let’s Code: Running a Regression Model

Running a Regression Model: A Simple Beginning

Running a Regression Model: Improving Our Model

Steps Prior to Analysis with Regression Models

2

Regressions 2: Using Regression to Estimate Causal Effects

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Using Regression to Get Causal Effects: Unconfoundedness

Regressing Soda pop and K-pop

Knowing it all

The Uncounfoundedness Assumption

How to Compute Regressions: Ordinary Least Squares (OLS)

Let’s Code: Toying with OLS - Outliers & Statistical Power

Toying with OLS I: Outliers

Toying with OLS II: Statistical Power

Let’s Code: Toying with OLS III - Model Selection

Toying with OLS III: Model Selection

Common Statistical Terms and Transformations in Regression Models

Idenfifying Non-Linear Relationships

Statistical Interactions in Regression Models

Let’s Code: Creating a Regression Model with Interaction Effects

Creating a Regression Model With Interaction Effects: Part 1

Creating a Regression Model With Interaction Effects: Part 2, Mediating and Moderating Effects

Logistic Regression Models

When to Use a Logistic Regression Model

Defining The Average Effect of Treatment on the Treated

Average Effect of Treatment on the Treated

How to Compute ATE Under Unconfoundedness, and What Not to Do

Let’s Code: Practice with Survey Weights

Practice with Survey Weights: Part 1

Practice with Survey Weights: Part 2

When Survey Weights Are Unnecessary

3

Regressions 3: Introduction to Matching Methods

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Matching Methods

Should Megan Use Matching Methods With Her Survey?

Can Your Survey Design Affect Your Matching Methods?

Problems with Matching Methods When Comparing Individuals?

The Lifetime Earnings of Veterans and Nonveterans

Why We Need Matching Methods

The Unconfoundedness Assumption in Angrist's Study on the Effect of Veteran Status on Lifetime Earnings

Replication and Validity

Causal Inference with Matching Methods

The Effect of Volunteer Military Service on Lifetime Earnings

Let’s Code: Communication Skills in Video Games

Communication Skills in Video Games: Do We Need to Use Matching Methods?

Communication Skills in Video Games: Propensity Score Matching in R

The Credibility of the Unconfoundedness Assumption

Assumptions and Causality

Causal Inference with R - Regression

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