causal-inference-guide
Causal inference methods including DiD, IV, RDD, and synthetic control
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Causal Inference Guide A skill for applying quasi-experimental causal inference methods in observational research. Covers difference-in-differences, instrumental variables, regression discontinuity designs, and synthetic control methods with implementation code and diagnostic checks. ## Difference-in-Differences (DiD) ### Classic Two-Period DiD ```python import numpy as np import pandas as pd im
What does the causal-inference-guide skill do?
Causal inference methods including DiD, IV, RDD, and synthetic control
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.