🇺🇸English | 李俊億

일상-카우스-스킬

실제로 작동했거나 운이 좋을까요?

전환 점프 12% 당신이 새로운 체크 아웃을 배송 주. 체크 아웃, 휴가 교통, 또는 당신의 경쟁자는 같은 주 가격을 상승? 질문에 답할 수 있 을 때까지, 당신은 정말 당신의 변경 "작업."라고 말할 수 없습니다.

이 플러그인은 코딩 에이전트를 제공합니다 워크플로우 I 사용: 당신이 가지고있는 데이터에 대한 올바른 방법을 선택, 답변을 신뢰하기 전에 가정을 확인, R 또는 Python의 분석을 작성, 그리고 스트레스 테스트 결과.

그것은 매일 Causal Inference에 동반자로 구축 된 AI 에이전트에 대 한 causal inference 플러그인입니다.

: Claude Code · Gemini CLI · GitHub Copilot CLI · Codex CLI · Cursor

** : ** 증거 기반 결정을 내릴 필요가있는 사람. 마케팅 및 성장, 제품, BI, 데이터 과학, 수익 및 ops, 정책 연구원 및 학생.

이 플러그인에서 얻는 것

플러그인은 5 단계로 작동하며, 질문에 답하고, 보고서를 작성합니다. 당신은 당신이 원하는 어떤 단계에서 선택할 수 있습니다.

예제 사용 사례 1: A/B 테스트

🇺🇸 English | 🇧🇷 Português (BR)

everyday-causal-skills

Did it actually work, or did you just get lucky?

Conversion jumped 12% the week you shipped the new checkout. Was it the checkout, the holiday traffic, or your competitor raising prices the same week? Until you can answer that question, you can’t really say your change “worked.”

This plugin gives your coding agent the workflow I use: pick the right method for the data you have, check the assumptions before you trust the answer, write the analysis in R or Python, and stress-test the result.

It’s a causal inference plugin for AI agents, built as a companion to Everyday Causal Inference.

Works with: Claude Code · Gemini CLI · GitHub Copilot CLI · Codex CLI · Cursor

Built for: anyone who needs to make evidence-based decisions. Marketing and growth, product, BI, data science, revenue and ops, policy researchers, and students.

What you get from this plugin

The plugin works in five steps, from refining the question you want to answer, to writing the report. You are free to pick and start from any step you like.

→ Describe your problem
→ Get a method recommendation
→ Check assumptions and structure the analysis
→ Stress-test the results
→ Write the executive report

Example use case 1: Designing an A/B test

An e-commerce team redesigned their checkout page and wants to know if it increases conversion before rolling it out to everyone. They’re not sure how long the test needs to run.

You: /causal-experiments We redesigned our checkout page and want to A/B test if it increases conversion. How long should we run the experiment?

The plugin asks a few follow-up questions in plain language: what’s your current conversion rate, how many visitors do you get per week, and what’s the smallest improvement that would make the redesign worth it. From your answers, it calculates the sample size and tells you how many weeks the test needs to run to detect that difference reliably.

Then it flags design decisions you might not have thought about — like whether to randomize by visitor or by session, and how to handle users who see both versions during the test.

You: We can randomize by visitor using a cookie. What about users who abandon and come back?

It walks you through those edge cases, writes the analysis code in R or Python, and builds in the checks you’ll need: balance diagnostics to make sure the groups are comparable, and a pre-registered analysis plan so you’re not fishing for results after the fact.

By the time you launch the test, the analysis is already written. When the data comes in, you run the code and get the answer.

Example use case 2: Measuring the impact of a loyalty program