Stat Causal Inference

What it does

When your agent uses it

• The user needs to determine if X caused Y from non-experimental data
• Evaluate program/policy impact without a randomized trial
• Control for confounders — even if they say did this change cause the improvement
• How do we measure the impact without an experiment

Example prompts

• “did this change cause the improvement”
• “how do we measure the impact without an experiment”
• “is this correlation or causation”
• “/stat-causal-inference”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Define the causal question: What is the treatment? What is the outcome?
2. 2Identify threats to validity: What confounders could explain the association?
3. 3Choose a method: Based on data structure and available identification strategy
4. 4Check assumptions: Each method has testable and untestable assumptions
5. 5Estimate the effect: Run the analysis
6. 6Sensitivity analysis: How much would results change if assumptions are partially violated?

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. 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

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  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

Stat Causal Inference loads about 1.2k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 136 tokens; SKILL.md has 372 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~136

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.2k

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

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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 372 words, ~1,162 tokens.

name

stat-causal-inference

description

Apply causal inference methods — counterfactual framework, instrumental variables, propensity score matching, and difference-in-differences — to estimate causal effects from observational data. Use this skill when the user needs to determine if X caused Y from non-experimental data, evaluate program/policy impact without a randomized trial, or control for confounders — even if they say 'did this change cause the improvement', 'how do we measure the impact without an experiment', or 'is this correlation or causation'.

metadata.category

WP-21 設計/資訊/傳播/公衛

metadata.tags

statistics, causal-inference, econometrics

IRON LAW: Correlation Is Not Causation — But Causation Is Estimable

Observational data cannot prove causation through correlation alone.
BUT with the right methodology (matching, IV, DID, RDD), we CAN
estimate causal effects from observational data — IF the assumptions
of each method are satisfied and explicitly tested.

The key question is always: "What would have happened WITHOUT the treatment?"
(the counterfactual)

# Causal Analysis: {Treatment} → {Outcome}

## Causal Question
- Treatment: {what intervention/event}
- Outcome: {what we're measuring}
- Counterfactual: {what would have happened without treatment}

## Identification Strategy
- Method: {PSM / IV / DID / RDD / etc.}
- Rationale: {why this method fits}
- Key assumption: {stated explicitly}
- Assumption test: {how we check, or acknowledge if untestable}

## Results
- Estimated causal effect: {magnitude with CI}
- Robustness checks: {alternative specifications}

## Limitations
{What could still invalidate these results}

© asgard-ai-platform, 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 3 other files (references) in stat-causal-inference of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/causal-dags.md
• references/did-implementation.md

Open the folder on GitHubat commit 4e7f4f8