/causal-inference-r

name: causal-inference-r
description: Run IV, DiD, and RDD analyses in R with proper diagnostics. Use when implementing causal inference methods, event studies, or treatment effect estimation.

R Econometrics

Purpose

This skill helps economists run rigorous econometric analyses in R, including Instrumental Variables (IV), Difference-in-Differences (DiD), and Regression Discontinuity Design (RDD). It generates publication-ready code with proper diagnostics and robust standard errors.

When to Use

• Running causal inference analyses
• Estimating treatment effects with panel data
• Creating publication-ready regression tables
• Implementing modern econometric methods (two-way fixed effects, event studies)

Instructions

Step 1: Understand the Research Design

Before generating code, ask the user: 1. What is your identification strategy? (IV, DiD, RDD, or simple regression) 2. What is the unit of observation? (individual, firm, country-year, etc.) 3. What fixed effects do you need? (entity, time, two-way) 4. How should standard errors be clustered?

Step 2: Generate Analysis Code

Based on the research design, generate R code that:

1. **Uses the `fixest` package** - Modern, fast, and feature-rich for panel data 2. **Includes proper diagnostics:**

• For IV: First-stage F-statistics, weak instrument tests
• For DiD: Parallel trends visualization, event study plots
• For RDD: Bandwidth selection, density tests

3. **Uses robust/clustered standard errors** appropriate for the data structure 4. **Creates publication-ready output** using `modelsummary` or `etable`

Step 3: Structure the Output

Always include:

# 1. Setup and packages
# 2. Data loading and preparation
# 3. Descriptive statistics
# 4. Main specification
# 5. Robustness checks
# 6. Visualization
# 7. Export results

Step 4: Add Documentation

Include comments explaining:

• Why each specification choice was made
• Interpretation of key coefficients
• Limitations and assumptions

Example Prompts

• "Run a DiD analysis with state and year fixed effects, clustering at the state level"
• "Estimate the effect of X on Y using Z as an instrument"
• "Create an event study plot showing treatment effects by year"
• "Run a sharp RDD with optimal bandwidth selection"

Example Output

# ============================================
# Difference-in-Differences Analysis
# ============================================

# Setup
library(tidyverse)
library(fixest)
library(modelsummary)

# Load data
df <- read_csv("data.csv")

# Prepare treatment variable
df <- df %>%
  mutate(
    post = year >= treatment_year,
    treated = state %in% treatment_states,
    treat_post = treated * post
  )

# ----------------------------------------
# Main DiD Specification
# ----------------------------------------

# Two-way fixed effects
did_model <- feols(
  outcome ~ treat_post | state + year,
  data = df,
  cluster = ~state
)

# View results
summary(did_model)

# ----------------------------------------
# Event Study
# ----------------------------------------

# Create relative time variable
df <- df %>%
  mutate(rel_time = year - treatment_year)

# Event study regression
event_study <- feols(
  outcome ~ i(rel_time, treated, ref = -1) | state + year,
  data = df,
  cluster = ~state
)

# Plot coefficients
iplot(event_study, 
      main = "Event Study: Effect on Outcome",
      xlab = "Years Relative to Treatment")

# ----------------------------------------
# Robustness: Alternative Specifications
# ----------------------------------------

# Different clustering
did_robust <- feols(
  outcome ~ treat_post | state + year,
  data = df,
  cluster = ~state + year  # Two-way clustering
)

# ----------------------------------------
# Export Results
# ----------------------------------------

modelsummary(
  list("Main" = did_model, "Two-way Cluster" = did_robust),
  stars = c('*' = 0.1, '**' = 0.05, '***' = 0.01),
  output = "results/did_table.tex"
)

Requirements

Software

• R 4.0+

Packages

• `fixest` - Fast fixed effects estimation
• `modelsummary` - Publication-ready tables
• `tidyverse` - Data manipulation
• `ggplot2` - Visualization

Install with:

install.packages(c("fixest", "modelsummary", "tidyverse"))

Best Practices

1. **Always cluster standard errors** at the level of treatment assignment 2. **Run pre-trend tests** for DiD designs 3. **Report first-stage F-statistics** for IV (should be > 10) 4. **Use `feols` over `lm`** for panel data (faster and more features) 5. **Document all specification choices** in your code comments

Common Pitfalls

• ❌ Not clustering standard errors at the right level
• ❌ Ignoring weak instruments in IV estimation
• ❌ Using TWFE with staggered treatment timing (use `did` or `sunab()` instead)
• ❌ Not reporting robustness checks

References

• [fixest documentation](https://lrberge.github.io/fixest/)
• [Cunningham (2021) Causal Inference: The Mixtape](https://mixtape.scunning.com/)
• [Angrist & Pischke (2009) Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)

Changelog

v1.0.0

• Initial release with IV, DiD, RDD support