Causal Inference Mixtape

CommunityPopular

brycewang-stanford

causal-inference-mixtape

This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.

Overview

Publisher	brycewang-stanford
Repository	`Auto-Empirical-Research-Skills`
Skill name	`causal-inference-mixtape`
Stars	3.8K
Forks	479
Bundled files	5
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Causal Inference Mixtape AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/10-Jill0099-causal-inference-mixtape

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git /tmp/Auto-Empirical-Research-Skills
mkdir -p .claude/skills
cp -r /tmp/Auto-Empirical-Research-Skills/skills/10-Jill0099-causal-inference-mixtape .claude/skills/causal-inference-mixtape

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Causal Inference Mixtape in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Causal Inference Mixtape on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Causal Inference Mixtape is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Causal Inference: The Mixtape — Code Skill

Practitioner-oriented causal inference skill built from Scott Cunningham's Causal Inference: The Mixtape repository. Covers 10 identification strategies with ready-to-run code templates in Python, R, and Stata.

Methods Covered

Method	Python	R	Stata	Reference
OLS / Regression	statsmodels	estimatr	reg/reghdfe	`references/method-patterns.md` §1
Difference-in-Differences	statsmodels + C()	lfe/fixest	xtreg/reghdfe	`references/method-patterns.md` §2
Event Study (Dynamic DiD)	manual lead/lag	estimatr	reghdfe	`references/method-patterns.md` §3
Staggered DiD / TWFE	statsmodels	bacondecomp	bacondecomp	`references/method-patterns.md` §4
Regression Discontinuity	statsmodels polynomial	rdrobust	rdplot/rdrobust	`references/method-patterns.md` §5
Instrumental Variables	linearmodels IV2SLS	AER/ivreg	ivregress 2sls	`references/method-patterns.md` §6
Synthetic Control	rpy2 → R Synth	Synth + SCtools	synth	`references/method-patterns.md` §7
Matching / PSM / IPW	manual logit + weights	MatchIt + Zelig	teffects/cem	`references/method-patterns.md` §8
DAGs / Collider Bias	dagitty (conceptual)	dagitty/ggdag	—	`references/method-patterns.md` §9
Randomization Inference	permutation loop	ri2	ritest	`references/method-patterns.md` §10

Core Workflow

Implement a Causal Method

Identify the method from the table above
Load the appropriate template from references/method-patterns.md
Adapt variable names, fixed effects, and clustering to the user's data
Add robustness checks (parallel trends for DiD, McCrary for RDD, first-stage F for IV)

Choose the Right Language

Scenario	Recommendation
ML pipeline integration	Python (statsmodels + linearmodels)
Synthetic Control	R (Synth package) or Stata (synth) — Python lacks mature implementation
Bacon decomposition	R (bacondecomp) or Stata — no Python equivalent
Publication-ready tables	Stata (outreg2/esttab) or R (stargazer/modelsummary)
Coarsened Exact Matching	Stata (cem) or R (MatchIt) — no Python equivalent
Quick prototyping	Python with statsmodels

Cross-Language Equivalents

Task	Python	R	Stata
OLS with robust SE	`smf.ols().fit(cov_type='HC1')`	`lm_robust()`	`reg y x, robust`
Cluster SE	`fit(cov_type='cluster', cov_kwds={'groups': g})`	`felm(y ~ x	0
Two-way FE	`C(id) + C(time)` in formula	`felm(y ~ x	id + time)`
IV / 2SLS	`IV2SLS.from_formula('y ~ 1 + exog + [endog ~ inst]')`	`ivreg(y ~ exog	inst)`
DiD	`C(treat)*C(post)`	`treat:post` in formula	`did_multiplegt` or interaction

Key Python Patterns

DiD with Cluster-Robust SE

python
import statsmodels.formula.api as smf

model = smf.ols('y ~ C(treated)*C(post) + controls', data=df)
results = model.fit(cov_type='cluster', cov_kwds={'groups': df['firm_id']})

Event Study (Lead/Lag)

python
# Create relative time dummies
for k in range(-4, 5):
    col = f'rel_{k}' if k >= 0 else f'rel_m{abs(k)}'
    df[col] = (df['relative_time'] == k).astype(int)

# Drop t=-1 as reference
formula = 'y ~ ' + ' + '.join([c for c in rel_cols if c != 'rel_m1']) + ' + C(id) + C(year)'

IV / 2SLS

python
from linearmodels.iv import IV2SLS

model = IV2SLS.from_formula('y ~ 1 + exog + [endog ~ instrument]', data=df)
results = model.fit(cov_type='clustered', clusters=df['cluster_var'])

Robustness Check Patterns

Method	Required Checks
DiD	Parallel trends (event study plot), placebo treatment dates
RDD	McCrary density test, bandwidth robustness (half/double IK optimal), polynomial robustness
IV	First-stage F > 10, exclusion restriction argument, over-identification test
Synthetic Control	Pre-treatment RMSPE, placebo distribution, leave-one-out
Matching	Covariate balance table, caliper sensitivity

Common Pitfalls

TWFE with staggered treatment — standard two-way FE is biased when treatment timing varies. Use Bacon decomposition or Sun & Abraham / Callaway & Sant'Anna estimators.
Synthetic Control with many treated units — the Synth package handles one treated unit. For multiple, use augmented synthetic control or stacked approach.
RDD without McCrary test — always test for manipulation at the cutoff before estimating.
IV weak instruments — report first-stage F-statistic. Below 10 indicates weak instrument bias.
Python Synth gap — no mature Python Synth package exists. Use rpy2 to call R's Synth from Python.

Additional Resources

Reference Files

references/method-patterns.md — Detailed code templates for all 10 methods with full examples
references/r-stata-comparison.md — Cross-language package comparison and method coverage gaps

Prompt Files

prompts/01-implement-method.md — Copy-paste prompt for implementing any causal method
prompts/02-robustness-checks.md — Copy-paste prompt for generating robustness check code

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Causal Inference Mixtape AI skill do?

Why use Causal Inference Mixtape on TypingMind?

Because you install it once and use it with any model. Causal Inference Mixtape is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Causal Inference Mixtape in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/10-Jill0099-causal-inference-mixtape. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Causal Inference Mixtape?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Causal Inference Mixtape?

As many as you like. As long as a model supports skills, you can use Causal Inference Mixtape with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Causal Inference Mixtape AI skill free?

It is published on GitHub by brycewang-stanford. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Causal Inference: The Mixtape — Code Skill

Methods Covered

Core Workflow

Implement a Causal Method

Choose the Right Language

Cross-Language Equivalents

Key Python Patterns

DiD with Cluster-Robust SE

Event Study (Lead/Lag)

IV / 2SLS

Robustness Check Patterns

Common Pitfalls

Additional Resources

Reference Files

Prompt Files

Bundled files

Frequently asked questions

Related AI Skills

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