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开发阶段案例 · 模型盲评尚待独立人工复核，不代表正式 Benchmark 结论。
自动补全Evidence 更优94 / 162 · 9728e36ee848f98c

Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code

统计学 · 2012.09920v2

FLOWING EVIDENCE BENCHMARK

怎么判断 Evidence 真的有帮助？

核心问题是：在同一写作位置、使用同一模型和任务时，提供检索文献片段会怎样改变首次输出？我们成对比较两种条件，保留持平、不可用和评审未完成的结果。

同一段原稿 · 比较是否提供 Evidence

01 · 固定写作位置

论文原稿

同一处写作点位 ▌

同一原稿位置的自动补全成对比较

02 · 构造两组输入

两组共用

原稿上下文、模型、任务和提示词

A · 提供 Evidence

额外提供检索文献片段

B · 不提供 Evidence

不提供检索文献片段

LLM

同一模型、同一版本

A → 首次输出

B → 首次输出

盲评 Agent

匿名标记两份首次输出为 X、Y

按续写质量判断：准确性、任务贴合度和可用性
输出：X 更优 / 持平 / Y 更优 / 两组均不可用

换序复评：X / Y → Y / X

盲评具体怎么判？

① 匿名两份输出
评审看到同一原稿和两份首次输出，但不知道哪份用了 Evidence。

② 比较并换序复评
根据当前任务的标准，按 X/Y、再按 Y/X 的顺序各评一次。

③ 复核分歧
两次结论不一致时再做第三次判定；未完成的评审也留在分母。

自动补全点位怎样分层？

在查看生成结果前，先核查检索片段是否含有能直接支撑下一步续写的具体命题；有则放在左侧机会组，否则放在右侧普通组。每个学科从可准入论文中均衡选取两组点位。50/50 是实验设计，不代表真实写作中两类点位各占一半。

AUTOCOMPLETE · 110

定量生物学、统计学、天体物理学

左侧 55 个、右侧 55 个点位；统计学采用 10 篇论文的复测结果。

AUTOCOMPLETE · 52

心理学与气候科学

左侧 26 个、右侧 26 个点位；心理学计入 9 篇，气候科学计入 4 篇。

表格里的百分比怎么算？

五学科共有 81 个左侧点位。原始盲评有 51 个 Evidence 胜出；任务校验将其中一条空白续写改归“两组均不可用”，因此公开统计为 50 个。普通组另有一条双方空白，已从“未分出胜负”改归“两组均不可用”。这些原始判定仍可在案例页查看。

50Evidence 版本更优
÷
81该层全部点位
=
62%该层 Evidence 获评更优的比例

来源贡献是另一项复核：已进入复核的 21 个 Evidence 获胜点位中，16 个确认直接使用了检索论文；另有 29 个胜出点位尚待复核。

当前是开发阶段的模型评审结果，尚未完成独立人工复核；这些数字不代表正式 Benchmark 结论，也不能单独证明因果关系。

原稿写作位置

原文摘录 · 非 PDF 页面

研究论文 · 原文片段

Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code

1 Introduction

… behavioral sciences are causal in nature. For example, what is the mortality risk difference amongst patients offered a surgical procedure versus those who did not receive it in a given population? [ 1 ] Causal inference methods may be used to answer this scientific question where a clinical trial is unfeasible or unethical (e.g. a surgical procedure). [ 2 ] Causal inference methods are based on the counterfactual framework introduced by Neyman in the randomized experiment setting, then extended to time-fixed and time-varying observational studies by Rubin and Robins, respectively. ( 3 , 4 )

Over the years, rapid on-going advances in the field causal inference have created a set of different approaches to estimate the causal effect of a treatment (or exposure) on an outcome (i.e., methods that incorporate propensity scores, the G-computation, or a combination of both, namely double-robust methods). Overall, double-robust methods now are preferred over naive regression approaches because the latter are biased under misspecification of a parametric outcome model when the research question is causal in nature. [ 5 , 6 ]

↓ 此处生成下一段续写

黄色标出写作点位附近的原文；本页根据评测输入重排文本，不是原始 PDF 截图。

同一写作位置的成对对照盲评结果：Evidence 更优

两组使用同一模型、同一写作任务和原稿上下文，仅比较是否提供检索文献片段。下方展示评测记录中的首次输出。

Evidence

同一模型与写作位置 · 提供检索文献片段 · 首次输出

盲评更优

Double-robust estimators combine an outcome regression model with a treatment (or exposure) model, so that the estimated causal effect remains consistent if either model—but not necessarily both—is correctly specified. [ 5 , 6 ] This property makes them attractive in observational settings where the true form of the outcome or treatment mechanism is unknown.

提供给 Evidence 版本的文献片段

Robust semiparametric estimators: missing data and causal inference ↗

Semiparametric inference with missing outcome data (including causal inference) is based on partially specified models which are not of direct interest (e.g., model for missingness/treatment assignment mechanism). Different class of estimators exist, which are more or less robust to misspecification…

展开完整文献摘录

Semiparametric inference with missing outcome data (including causal inference) is based on partially specified models which are not of direct interest (e.g., model for missingness/treatment assignment mechanism). Different class of estimators exist, which are more or less robust to misspecification of these models. Another type of threat to the validity of the inference occur in situations where some observations are contaminated (generated by some nuisance distribution). Classical semiparametric inference is not robust to such contamination, and a single observation may have an arbitrary large effect on bias as measured by the influence function. We introduce inverse probability weighted, double robust and outcome regression estimators of location and scale parameters, which are robust to contamination in the sense that their influence function is bounded.

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

本次未获偏好

Despite these methodological advances, applied researchers face practical barriers to implementing causal inference methods, including the need to navigate multiple software environments and reconcile differences in estimation and reporting across them. This tutorial addresses that gap by providing a unified, reproducible workflow for computational causal inference in Stata, R and Python.

评审结论与任务校验

模型盲评判定“Evidence 更优”。下方保留评审原始理由（英文），供核对判断依据。

查看模型评审原始理由（英文）

原始理由中的 Output A = Evidence，Output B = 无 Evidence。

Output A provides a coherent, grounded continuation that elaborates on double-robust methods—the topic just introduced in the draft's final sentence. It explains what makes double-robust estimators 'preferred' (consistent if either model is correct) and connects this to 'observational settings,' which aligns with the draft's focus on causal inference when trials are unfeasible. The source snippet supports this by discussing 'double robust' estimators and their robustness properties. No unsupported claims: the explanation of double-robustness is a standard characterization, and the source mentions this class of estimators. Output B introduces an entirely new topic (software barriers, a tutorial, Stata/R/Python workflow) with no grounding in the draft or source. The draft never mentions software, tutorials, or reproducible workflows; this is a non-sequitur that changes the manuscript's direction. The source also does not mention software or tutorials. B's claim that 'this tutorial addresses that gap' is unsupported—the draft does not establish that this is a tutorial paper, and no source supports this framing.

文献片段是输入材料；出现于此不代表输出使用了它，也不代表它能够支持全部主张。原文与检索片段经过截取；页面没有展示模拟分数或模拟 PDF。