Example Datasets
pymc-labs/CausalPy
Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes.
Agent skill
Causal Inference
by brycewang-stanford in brycewang-stanford/Auto-Empirical-Research-Skills
Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy.
Install Causal Inference
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .claude/skills/causal-inference && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .claude/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inferenceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .agents/skills/causal-inference && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .agents/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .cursor/skills/causal-inference && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .cursor/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git --path skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .gemini/skills/causal-inference && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .gemini/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inferenceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .github/skills/causal-inference && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .github/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference .opencode/skills/causal-inference && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "causal-inference" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference into .opencode/skills/causal-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-inference", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
causal-inference- GitHub stars
- 4.6k
- Token cost
- ~2k tokens
- SKILL.md length
- 853 words
- Files
- 7 (incl. references)
- Skills in repo
- 383
- Repo updated
- First seen
- Licence
- MIT
At a glance
Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy.
- Works in 8 steps: Formulate the causal question — Propose… → Draw the DAG — Propose causal graph with… → Identify — Determine identification… → …
- : causal inference
- SKILL.md covers Dependencies, Workflow overview, Design selection guide and Critical rules, plus 2 more sections
- Calls git; reaches github.com
What it does
Causal Inference is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs…
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `README.md`, `references/dags-and-identification.md` and `references/quasi-experiments.md`).
It sits in Research & Science, covering Econometrics and empirical research. It works with PyMC. The repository describes itself as: 🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI… The licence is MIT.
When your agent uses it
- : causal inference
- Causal effect estimation
- Treatment effects
- Counterfactuals
Example prompts
- “does X cause Y”
- “what is the effect of X on Y.”
- “/causal-inference”
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Formulate the causal question — Propose precise estimand (ATE, ATT, LATE, etc.). ⚠️ ASK USER TO CONFIRM.
- Draw the DAG — Propose causal graph with nodes, edges, and explicit non-edges. ⚠️ ASK USER TO CONFIRM. See…
- Identify — Determine identification strategy (backdoor, front-door, IV, RDD, DiD). ⚠️ ASK USER TO CONFIRM untestable assumptions. See…
- Choose design — Match problem to method using table below. ⚠️ ASK USER TO CONFIRM. See references/quasi-experiments.md or…
- Estimate — Build and fit the model. Delegate all PyMC mechanics to bayesian-workflow skill.
- Refute — MANDATORY. Run design-specific robustness checks. See references/refutation.md
- Interpret — Effect size + decision-relevant HDIs + probability of direction.
- Report — Generate causal analysis report. See references/reporting.md
What it can do on your machine
Read from SKILL.md and the folder at commit 9fa87d8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Causal Inference loads about 2k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 202 tokens; SKILL.md has 853 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Auto-Empirical-Research-Skills at commit 9fa87d8, republished under its MIT licence (© brycewang-stanford). 853 words, ~2,049 tokens.
Download SKILL.mdSave it as
.claude/skills/causal-inference/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- causal-inference
- description
- Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."
- license
- MIT
- metadata.author
- [Alexandre Andorra](https://alexandorra.github.io/)
- metadata.version
- 1.0
Causal Inference
Dependencies
This skill requires the bayesian-workflow skill for all PyMC modeling steps (priors, sampling, diagnostics, calibration, reporting).
Detect it:
ls ~/.claude/skills/bayesian-workflow/SKILL.md 2>/dev/null || ls .claude/skills/bayesian-workflow/SKILL.md 2>/dev/nullIf not found, install it:
git clone https://github.com/Learning-Bayesian-Statistics/baygent-skills.git /tmp/baygent-skills
cp -r /tmp/baygent-skills/bayesian-workflow ~/.claude/skills/For all PyMC modeling steps (priors, sampling, diagnostics, calibration, reporting), follow the bayesian-workflow skill.
Workflow overview
Every causal analysis follows this sequence. Steps 1-4 are the thinking phase (no code). Steps 5-8 are the doing phase. Think before you do.
- Formulate the causal question — Propose precise estimand (ATE, ATT, LATE, etc.). ⚠️ ASK USER TO CONFIRM.
- Draw the DAG — Propose causal graph with nodes, edges, and explicit non-edges. ⚠️ ASK USER TO CONFIRM. See references/dags-and-identification.md
- Identify — Determine identification strategy (backdoor, front-door, IV, RDD, DiD). ⚠️ ASK USER TO CONFIRM untestable assumptions. See references/dags-and-identification.md
- Choose design — Match problem to method using table below. ⚠️ ASK USER TO CONFIRM. See references/quasi-experiments.md or references/structural-models.md
- Estimate — Build and fit the model. Delegate all PyMC mechanics to bayesian-workflow skill.
- Refute — MANDATORY. Run design-specific robustness checks. See references/refutation.md
- Interpret — Effect size + decision-relevant HDIs + probability of direction.
- Report — Generate causal analysis report. See references/reporting.md
Design selection guide
| Design | Use when | Key assumption | Tool |
|---|---|---|---|
| DiD | Treatment at known time, control group available | Parallel trends | CausalPy |
| Staggered DiD | Treatment rolls out at different times | Parallel trends per cohort | CausalPy |
| Synthetic Control | Single treated unit, donor pool available | Weighted donors approximate counterfactual | CausalPy |
| ITS | Time series, intervention at known time, no control | No confounding event at treatment time | CausalPy |
| RDD | Treatment by threshold on running variable | No manipulation at threshold | CausalPy |
| IV | Endogenous treatment, valid instrument | Exclusion restriction, relevance | CausalPy |
| IPSW | Observational data, treatment modeled | No unmeasured confounders, positivity | CausalPy |
| Structural (do/observe) | Full causal theory, model mechanisms | Correct DAG specification | PyMC |
| Counterfactual | "What would Y have been if X differed?" | Correct structural model | PyMC |
Critical rules
- No estimation without a confirmed DAG. A causal graph is not optional decoration — it makes assumptions explicit and determines the adjustment set. If the user resists, explain why the DAG is non-negotiable before proceeding.
- No causal claims without refutation. Every design has failure modes. Run at minimum one design-specific robustness check (placebo test, sensitivity analysis, falsification test) before reporting results. See references/refutation.md.
- State assumptions before results. Lead with what must be true for the estimate to be causal. Bury the estimate after the assumptions, not before. This is not optional politeness — it prevents misuse of results.
- Adapt HDIs to the decision context. The bayesian-workflow skill's 94% HDI is a sensible default; adapt it with explicit explanation when the decision stakes warrant it (e.g., 89% for exploratory, 97% for high-stakes policy). Report multiple intervals when the decision threshold matters.
- Downgrade causal language when warranted. If identification assumptions are unverifiable or refutation raises flags, soften claims: "consistent with a causal effect" not "causes", "estimated effect" not "true effect". Flag uncertainty loudly in the report.
- Ask the user when domain knowledge is needed. You cannot know whether an instrument is valid, whether parallel trends holds, or whether a confounder exists without domain expertise. Ask before assuming.
- Delegate PyMC mechanics to bayesian-workflow. This skill handles causal structure and design. The bayesian-workflow skill handles priors, sampling, diagnostics, calibration, and reporting format. Don't duplicate those rules here.
Show full SKILL.md (326 more words)Show less
Common gotchas
These are battle-tested lessons that save hours of debugging:
- CausalPy formula syntax uses
C()for categoricals. Passing a string column directly withoutC()will silently produce wrong dummy coding. Always wrap categorical treatment and group variables:"y ~ C(treatment) + C(group)". - DoWhy requires explicit
Unodes for unobserved confounders. Omitting them from the graph will make DoWhy treat your model as fully identified when it isn't. Add latent nodes explicitly and mark them as unobserved. - CausalPy's PyMC models don't auto-store log-likelihood. Same issue as bayesian-workflow:
nutpie silently drops it. Call
pm.compute_log_likelihood(idata, model=model)after sampling if you need it for model comparison. - Parallel trends is untestable in the post-treatment period. Pre-treatment trend tests are necessary but not sufficient — passing them doesn't prove the assumption holds after treatment. State this explicitly in every DiD report.
- Synthetic control requires the treated unit to lie within the convex hull of donors. If the treated unit is an outlier (highest GDP, largest city), no weighted combination of donors can approximate its counterfactual. Check this before running — if violated, the design is invalid.
- DiD group variable must be dummy-coded (0/1). CausalPy rejects string labels like "treatment"/"control". Use integers: 1 = treatment, 0 = control. Data also requires a
unitcolumn. - SyntheticControl expects wide-format data. Index = time, columns = unit names, values = outcome. If your data is long format, pivot first:
df.pivot(index="date", columns="unit", values="outcome").
When things go wrong
| Symptom | Likely cause | Fix |
|---|---|---|
| Refutation fails | Assumption violated | Diagnose which assumption, try alternative design or sensitivity bounds |
| DiD effect at placebo time | Parallel trends violated | Try synthetic control or add group-specific time trends |
| RDD: bunching at threshold | Manipulation of running variable | Design is invalid for this threshold — report and stop |
| SC: poor pre-treatment fit | Donors don't span treated unit | Add donors, expand donor pool, or reconsider design |
| DoWhy says "not identifiable" | Insufficient adjustment set | Revise DAG, add measured variables, or change design |
| CausalPy formula error | Wrong formula syntax | Use C() for categoricals, check variable names match dataframe columns |
© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (references) in skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference of brycewang-stanford/Auto-Empirical-Research-Skills.
- SKILL.md
- README.md
- references/dags-and-identification.md
- references/quasi-experiments.md
- references/refutation.md
- references/reporting.md
- references/structural-models.md
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
Causal Inference next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Causal Inference this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~2k | Automated safety check: Pass | MIT | |
| Example Datasetspymc-labs/CausalPy | 1.2k | — | ~587 | Automated safety check: Pass | Apache-2.0 | |
| PymcK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.7k | Automated safety check: Notes | Apache-2.0 | |
| Stata Data Cleaningmeleantonio/awesome-econ-ai-stuff | 646 | 2 repos | ~1.8k | Automated safety check: Pass | Custom licence | |
| Ectheory Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~905 | Automated safety check: Pass | None | |
| Jape Identification Strategyfranklee16/academic-research-skills | 223 | 1 repos | ~707 | Automated safety check: Pass | None |
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Related
Works with
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Questions about Causal Inference
What does Causal Inference do?
Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Causal Inference is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy.
When should I use Causal Inference?
Causal Inference fits situations like: : causal inference; causal effect estimation; treatment effects; counterfactuals.
How do I install Causal Inference in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a claude-code`. Or copy the skill folder (skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/causal-inference in your project. Claude Code loads it when a task matches its description.
How do I install Causal Inference in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a codex`. Or copy the skill folder (skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/causal-inference in your project. Codex loads it when a task matches its description.
Can I use Causal Inference in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/causal-inference, .gemini/skills/causal-inference, .github/skills/causal-inference and .opencode/skills/causal-inference in your project.
What does Causal Inference need to run?
Going by SKILL.md and its folder, Causal Inference needs the command-line tools its instructions call (git).
Does Causal Inference access the network?
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Causal Inference safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Causal Inference use?
Causal Inference is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Causal Inference use?
About 2k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17k tokens, read only when the agent opens those files.
What are the alternatives to Causal Inference?
Skills that share tags, products or a category with Causal Inference: Example Datasets (pymc-labs/CausalPy, 1.2k stars), Pymc (K-Dense-AI/scientific-agent-skills, 48k stars), Stata Data Cleaning (meleantonio/awesome-econ-ai-stuff, 646 stars) and Ectheory Data Analysis (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Causal Inference?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Auto-Empirical-Research-Skills, which has 4,556 GitHub stars. The repository holds 383 skills in this directory. The repository was last updated on October 5, 2026.
Source: brycewang-stanford/Auto-Empirical-Research-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.