causal-inference-analysis
CommunityMaster causal inference methods.
AuthorHaipingXu
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides practical guidance and code examples for implementing complex causal inference methodologies, enabling researchers to rigorously estimate treatment effects.
Core Features & Use Cases
- Method Implementation: Offers Python and Stata code for Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Synthetic Control (SC).
- Assumption Testing: Guides users on essential diagnostic tests like pre-trend analysis, first-stage F-statistics, and covariate balance.
- Output Generation: Includes examples for creating publication-ready regression tables and event study plots.
- Use Case: A researcher needs to estimate the causal effect of a new policy using a staggered adoption design. This Skill provides the necessary
pyfixestcode for an event study analysis and guidance on interpreting the results.
Quick Start
Use the causal-inference-analysis skill to implement a two-way fixed effects difference-in-differences model in Python using linearmodels.
Dependency Matrix
Required Modules
linearmodelspyfixesteconmldoublemlrdrobustrddensitystargazerpysynthsynth_runnerpystatapyreadstat
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: causal-inference-analysis Download link: https://github.com/HaipingXu/social-science-claude-scholar/archive/main.zip#causal-inference-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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