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Causal Inference with R - Regression

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

This exercise is part of the course

Causal Inference with R - Regression

BeginnerSkill Level

5.0+

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This chapter will introduce you to using regression analysis to find causal effects

Exercise 1: Course Trailer for Causal Inference with R - Regression Exercise 2: Introduction to Regression Analysis Exercise 3: Interpreting Regressions Exercise 4: Reversing Causal Direction Exercise 5: Let’s Code: Comparing Hospital Quality to Mortality Rates Exercise 6: Comparing Hospital Quality to Mortality Rates Exercise 7: Regression Models and Policy Implementation Exercise 8: Basic Elements of a Regression Table Exercise 9: Reading a Bivariate Regression Table Exercise 10: Reading a Multivariate (Multiple) Regression Table Exercise 11: The Relationship between Economic Development and Property Rights Exercise 12: Regressions with small coefficients and small confidence intervals Exercise 13: Recalling terminology Exercise 14: Multiple Regression Models Exercise 15: Let’s Code: Running a Regression Model Exercise 16: Running a Regression Model: A Simple Beginning Exercise 17: Running a Regression Model: Improving Our Model Exercise 18: Steps Prior to Analysis with Regression Models

This chapter will introduce you to using regression analysis to find causal effects

Exercise 1: Using Regression to Get Causal Effects: Unconfoundedness Exercise 2: Regressing Soda pop and K-pop Exercise 3: Knowing it all Exercise 4: The Uncounfoundedness Assumption Exercise 5: How to Compute Regressions: Ordinary Least Squares (OLS)Exercise 6: Let’s Code: Toying with OLS - Outliers & Statistical Power Exercise 7: Toying with OLS I: Outliers Exercise 8: Toying with OLS II: Statistical Power Exercise 9: Let’s Code: Toying with OLS III - Model Selection Exercise 10: Toying with OLS III: Model Selection Exercise 11: Common Statistical Terms and Transformations in Regression Models Exercise 12: Idenfifying Non-Linear Relationships Exercise 13: Statistical Interactions in Regression Models Exercise 14: Let’s Code: Creating a Regression Model with Interaction Effects Exercise 15: Creating a Regression Model With Interaction Effects: Part 1 Exercise 16: Creating a Regression Model With Interaction Effects: Part 2, Mediating and Moderating Effects Exercise 17: Logistic Regression Models Exercise 18: When to Use a Logistic Regression Model Exercise 19: Defining The Average Effect of Treatment on the Treated Exercise 20: Average Effect of Treatment on the Treated Exercise 21: How to Compute ATE Under Unconfoundedness, and What Not to Do

Current Exercise

Exercise 22: Let’s Code: Practice with Survey Weights Exercise 23: Practice with Survey Weights: Part 1 Exercise 24: Practice with Survey Weights: Part 2 Exercise 25: When Survey Weights Are Unnecessary

This chapter will introduce you to using matching methods to find causal effects

Exercise 1: Matching Methods Exercise 2: Should Megan Use Matching Methods With Her Survey?Exercise 3: Can Your Survey Design Affect Your Matching Methods?Exercise 4: Problems with Matching Methods When Comparing Individuals?Exercise 5: The Lifetime Earnings of Veterans and Nonveterans Exercise 6: Why We Need Matching Methods Exercise 7: The Unconfoundedness Assumption in Angrist's Study on the Effect of Veteran Status on Lifetime Earnings Exercise 8: Replication and Validity Exercise 9: Causal Inference with Matching Methods Exercise 10: The Effect of Volunteer Military Service on Lifetime Earnings Exercise 11: Let’s Code: Communication Skills in Video Games Exercise 12: Communication Skills in Video Games: Do We Need to Use Matching Methods?Exercise 13: Communication Skills in Video Games: Propensity Score Matching in R Exercise 14: The Credibility of the Unconfoundedness Assumption Exercise 15: Assumptions and Causality