Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Nero1688/claude-academic-skills --skill causal-inference-architectgit clone --depth 1 https://github.com/Nero1688/claude-academic-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nero1688/claude-academic-skills/causal-inference-architect)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/causal-inference-architect"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/causal-inference-architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/causal-inference-architect"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/causal-inference-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00591 | $0.02686 |
| Opus 5.5 | $0.00236 | $0.01074 |
| Sonnet 5.5 | $0.00118 | $0.00537 |
| Haiku 4.5 | $0.00059 | $0.00269 |
Grade A, and why
causal-inference-architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 18d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
因果推論架構師(Causal Inference Architect)
Step 1|因果問題定式(不清楚就不往下)
寫成一句話:「〔處理 D〕對〔結果 Y〕在〔母體〕的效果,處理時點是〔何時/是否交錯〕, 反事實是〔誰〕」。三個立即檢查:
- D 的變異來源是什麼?(政策強制/自願採用/門檻規則)——變異來源決定策略。
- 自願採用=自選擇,DiD 只處理「時間不變」的選擇,時變選擇要正面交代。
- 預期效果的時間形狀?(立即/漸進/暫時)——決定事件窗與動態設定。
事件日哪裡來(台灣研究):短窗事件研究的事件日,首選公司官方揭露——
MOPS 重大訊息(公司「宣布」那一刻=事件日),乾淨且免費。要取得乾淨事件檔
先走 public-disclosure-scout,它把揭露整成 ticker, event_date, subject 直接餵本 skill。
Step 2|識別策略決策樹
| 變異型態 | 首選策略 | 關鍵假設(寫進論文的話) |
|---|---|---|
| 多單位、不同時點採用 | 交錯 DiD(現代估計量,見 Step 3) | 平行趨勢(條件於共變數) |
| 單一時點政策、全體受影響 | 事前事後+控制組(古典 DiD)/ SCM | 平行趨勢 / 供體池可組合反事實 |
| 連續指標跨門檻觸發處理 | RDD(sharp/fuzzy) | 門檻附近局部隨機、無精準操縱(McCrary 檢定) |
| 有外生工具 | IV/2SLS | 相關性(第一階段 F)+排除限制(只能論證不能檢定) |
| 單一處理單位(一家公司/一個縣市) | 合成控制 SCM | 供體池、前期擬合良好 |
| 揭露/公告事件看股價反應(短窗數日;長窗 12–36 月) | 事件研究:短窗市場模型 AR/CAR+BMP(叢集時 Kolari-Pynnönen 調整);長窗 calendar-time 組合為主、BHAR 為輔——窗口、模型、檢定與台灣情境見 references/event-study-estimation.md,CAAR 圖用 management-figure 的 car_plot() |
事件日精確(13:30 後順延)、窗內無混淆事件、估計窗與事件窗不重疊;長窗另需正常報酬模型正確 |
| 都沒有 | 誠實承認做關聯研究,用選擇模型/配對減緩,不宣稱因果 | — |
Step 3|現代 DiD 正典(交錯採用必讀,2020 後革命的核心)
- TWFE 的病:交錯採用+效果異質時,雙向固定效果把「已處理者當控制組」, 權重可為負,估計值可能連正負號都錯。先跑 Goodman-Bacon 分解看壞比較佔多少。
- estimator 選擇(細節與語法見
references/estimator-playbook.md):- Callaway & Sant'Anna(組別×時期 ATT,再聚合)——預設首選,控制組用 never-treated 或 not-yet-treated 要明說。
- Sun & Abraham(交互加權事件研究)——要動態路徑時。
- Stacked DiD——直觀穩健的備援,樣本量夠時。
- 事件研究圖是必需品不是裝飾:前期係數應近 0(pre-trends),圖要含信賴區間; 前期不平行不能只說「大致平行」——用 Rambachan & Roth 誠實區間量化「偏離多少 仍成立」。
- 全部交給 management-figure 的
event_study_plot()出投稿級事件研究圖（參考期、處理時點線、前期陰影、圖註自動印前期聯合檢定 p 與 M̄；估計量名稱進圖註）。
Step 4|診斷與安慰劑矩陣(審稿人一定要的)
| 診斷 | 做法 |
|---|---|
| 平行趨勢 | 事件研究前期係數+聯合檢定;誠實區間敏感度 |
| 安慰劑-時間 | 假處理時點提前 k 年,效果應消失 |
| 安慰劑-單位 | 未處理單位隨機指派假處理(SCM 標配 permutation) |
| 組成穩定 | 進出樣本的公司是否隨處理改變(balanced vs unbalanced 並陳) |
| 預期效應 | 採用「宣布」與「生效」分開檢驗 |
| IV 專屬 | 第一階段 F(弱工具用 effective F);過度識別檢定的侷限誠實說 |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 18d ago Changed · +4 lines · +135 tokens per session 66bbc2e4e3e3
- 1mo ago Changed · +8 lines bff32536d0cc
- 1mo ago First seen · 75 lines · 456 tokens per session scan A d4fc16385a4a
causal-inference-architect is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 591 tokens to every session and 2,686 once invoked, about $0.0024 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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