Run event studies and DiD analyses with robust estimators in R.
npx skills add https://github.com/dariia-m/my_claude_skills --skill event-study
Please help me install this Agent Skill. Skill: event-study Source: https://github.com/dariia-m/my_claude_skills/tree/main/event-studies Command: npx skills add https://github.com/dariia-m/my_claude_skills --skill event-study
This Skill provides a comprehensive toolkit for conducting event studies and difference-in-differences (DiD) analyses, enabling you to estimate dynamic treatment effects and diagnose potential issues in panel data.
Use the event-study skill to create an event study plot for the 'outcome' variable using 'treat_year' as the treatment timing and 'unit_id' and 'year' for panel structure.
High-intent search queries and answers about installing and using this skill.
To run a difference-in-differences analysis with staggered timing in R, use modern robust estimators like Callaway & Sant'Anna or Sun & Abraham, which handle heterogeneous treatment effects better than traditional TWFE models.
The best way to create an event study plot for panel data is using R packages like fixest and ggplot2 to visualize dynamic treatment effects over time relative to the treatment event.
To test for pre-trends in a DiD analysis, perform pre-trend testing and placebo checks using built-in diagnostics to validate that the parallel trends assumption holds before interpreting treatment effects.
Yes, this event study approach supports sensitivity analysis for treatment effects by utilizing the HonestDiD R package alongside standard robust estimators to check the stability of your findings.
You should use Callaway & Sant'Anna instead of standard TWFE models for an event study when your treatment timing is staggered, as it avoids negative weighting issues caused by heterogeneous effects.
Jonathan0823Reproduce bugs, analyze root causes, implement fixes, and verify with regression tests.
ya-wayGenerate publication-quality ggplot2 figures from GO, KEGG, and GSEA enrichment results.
cynkraReplace legacy R tidyverse idioms with modern dplyr, purrr, and stringr patterns.
xjtulycEstimates heterogeneous treatment effects using DID, TWFE event-study validation, Callaway-Sant'Anna staggered adoption, Bacon decomposition, and placebo tests for panel data in econometrics workflows with Python linearmodels and optional R packages.
