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日常カジュアルスキル

実際に仕事をしていたり、ラッキーになったんですか?

変換は、新しいチェックアウトを出荷週に12%ジャンプしました。 今週のチェックアウト、休日のトラフィック、または競合他社のライジング価格でしたか? その質問に答えることができるまで、あなたは本当にあなたの変更が「働いた」と言うことはできません。

このプラグインは、使用するワークフローをコーディングエージェントに与えます: 持っているデータの正しいメソッドを選択し、回答を信頼する前に、仮定を確認して、R または Python で解析を書き、結果をストレステストします。

日常の因果推論の仲間として構築されたAIエージェントの因果推論プラグインです。

仕事: クロード コード ・ ジェミニ CLI ・ GitHub Copilot CLI ・ コーデックス CLI ・ カーソル

: 証拠に基づく決定を下す必要がある人 マーケティングと成長、製品、BI、データサイエンス、収益、Ops、政策研究者、学生。

このプラグインから得られるもの

プラグインは、5つのステップで機能します。, 回答したい質問の精査から, レポートを書く. 好きなステップから自由に選ぶことができます。

##例ユースケース1:A/Bテストの設計

🇺🇸 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