Menu

Individual skill

causal-inference

0upvotes

Selects and executes a credible causal identification strategy - RCT, natural experiment, difference-in-differences, regression discontinuity, instrumental variables, or matching - ranked by assumption strength, with a confounder checklist, falsification tests, and a defensible effect estimate. Use when someone asks "did X actually cause Y", "how do I measure impact without an A/B test", "is this correlation causal", or needs to defend an effect estimate from observational data. Do NOT use for designing the data-collection study itself (sampling, instruments, power) - use primary-research instead; for reading out an already-run A/B test, use ab-test-analyzer instead.

skills/causal-inference/SKILL.md

Read pinned source

Source commit 97a13607d669157a4a16d5ba79b77f208319f7b9

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 6, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
24
Repository last updated
Oct 13, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Causal Inference

A confident effect estimate built on a broken identification strategy is worse than no estimate: it triggers real decisions with fake evidence. This skill forces the choice of design before any regression is run, states the assumptions each design buys, and requires falsification tests before an effect is reported as causal.

## Inputs to collect

Gather these before choosing a design. If the user cannot supply one, propose a default and label it a guess.

1. **Treatment**: the intervention, precisely defined (who received it, when, at what intensity).
2. **Outcome**: the measured variable and its timing relative to treatment.
3. **Unit of analysis**: person, account, store, region - and how many units exist on each side.
4. **Assignment mechanism**: how units ended up treated - randomized, threshold rule, policy rollout, self-selection. This single fact determines which designs are available.
5. **Data availability**: pre-treatment periods (how many), untreated comparison units, candidate instruments, covariates.
6. **Decision at stake**: what action the estimate will drive, and how costly a wrong sign or 2x-off magnitude would be.
Back to top