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Causal Inference with R - Instrumental Variables & RDD

Indirect Inference

This exercise is part of the course

Causal Inference with R - Instrumental Variables & RDD

BeginnerSkill Level

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This chapter will introduce you to Instrumental Variables (IV) analysis, a tricky but powerful method to find causality through indirect inference

Exercise 1: Course Trailer for Causal Inference with R – Instrumental Variables & RDD Exercise 2: The Logic of Instrumental Variables Exercise 3: Visual Logic of Instrumental Variables Exercise 4: What is the Relationship Between the Instrument and the Outcome?Exercise 5: What is the Relationship Between the Instrument and the Treatment?Exercise 6: What is the Relationship Between the Instrument and the Other Pre-Treatment Variables?Exercise 7: How Do We Calculate a Causal Effect with Instrumental Variables Analysis?Exercise 8: Some Instrumental Variables Terminology Exercise 9: Is it Endogenous vs Exogenous Variation?Exercise 10: Instrumental Variables in Action: Education and Wages (graphs)Exercise 11: Instrumental Variables in Action: Education and Wages (tables)Exercise 12: What if Our Data Shows No Clear Correlation Between Treatment and Outcome?Exercise 13: The Three Instrumental Variables Assumptions Exercise 14: Alan the Egg Farmer Turns to Instrumental Variables

This chapter covers more details about Instrumental Variables Analysis, and allows you to practice instrumental variables for yourself

Exercise 1: The Problem of Weak Instruments Exercise 2: An Example of a Weak Instrument Exercise 3: Overview of Research on Property Rights & Economic Development Exercise 4: Where Do Instruments Come From?Exercise 5: Using IV When There is Measurement Error Exercise 6: Logical Arguments About Instrumental Variables Exercise 7: Let's Code: Practice Using Instrumental Variables Exercise 8: Practice Using Instrumental Variables: CreditCo Exercise 9: Refutability and Nonrefutability of the Instrumental Variables Assumptions Exercise 10: Questioning the Validity of Instrumental Variables Results Exercise 11: Indirect Inference

Current Exercise

Exercise 12: Let's Code: Practice Computing Causal Effects Using Indirect Inference Exercise 13: Practice Computing Causal Effects Using Indirect Inference: CreditCo Exercise 14: Two Stage Least Squares (2SLS)Exercise 15: Overview of Squatters & Property Rights Experiment Exercise 16: Example of IV: Property Rights & Market Beliefs Exercise 17: Let's Code: Practice Computing Causal Effects Using 2SLS Exercise 18: Practice Computing Causal Effects Using Two-Stage Least Squares (2SLS): CreditCo Exercise 19: Comparing the Estimates from Indirect Inference and 2SLS Exercise 20: Solving Noncompliance in Experiments with Instrumental Variables Exercise 21: Overview of the Oregon Healthcare Experiment Exercise 22: Using IV to Solve Noncompliance in the Oregon Heathcare Insurance Experiment Exercise 23: Balancing Assumptions and Data When Using IV Exercise 24: Let's Code: Practice Using IV to Solve Noncompliance in the OHIE Exercise 25: Practice Using IV to Solve Noncompliance in the OHIE

This chapter introduces you to Regression Discontinuity Design (RDD), which is often considered a more flexible relative of Instrumental Variables

Exercise 1: Regression Discontinuity: Looking at People on the Edge Exercise 2: Regression Discontinuity: More Analysis of Thistlethwaite and Campbell Exercise 3: Key Variables in an RD Design Exercise 4: Identifying Key Variables in an RD Design Exercise 5: How to Compute Causal Effects in a Regression Discontinuity Analysis Exercise 6: Visualizing RD Designs Exercise 7: Let's Code: Examining Manipulation in Regression Discontinuity Design Exercise 8: Examining Manipulation in Regression Discontinuity Design Exercise 9: Using RDD to Study Neighborhoods and Schools Exercise 10: Thinking about confounders in an RD design Exercise 11: Using Geographical Borders More Generally in RD Designs Exercise 12: Let's Code: Practice Computing Regression Discontinuity Effects Exercise 13: Practice Computing Regression Discontinuity Effects Exercise 14: "Fuzzy" Regression Discontinuity: Addressing Blurry Lines Between Groups Exercise 15: Identifying Fuzzy and Sharp RDDs Exercise 16: Fuzzy RDD and Swiss Religion: Checking the Numbers

This chapter explains the treatment effect we are estimating with the instrumental variables method - LATE, or the Local Average Treatment Effect

Exercise 1: Which Causal Effects Are We Actually Getting with Instrumental Variables?Exercise 2: Defining LATE: The Local Average Treatment Effect Exercise 3: LATE and the ATE for Always-Takers Exercise 4: The "No Defiers" Assumption Exercise 5: Computing LATE, Part 1: Dividing the Population Exercise 6: Computing LATE, Part 2: Finding the Compliers Exercise 7: Computing LATE, Part 3: Getting a Result Exercise 8: The Pros & Cons of LATE Exercise 9: Interpreting Different Results when Working with Noncompliance Exercise 10: ATEs, CATEs, and LATEs: What's the Difference?Exercise 11: An Alternate Way to LATE?