1197 lines
29 KiB
Markdown
1197 lines
29 KiB
Markdown
# 因果分析日志 (v2)
|
||
|
||
## 日志说明
|
||
本文档记录 LLM 进行因果分析时的所有输入参数和输出结果。
|
||
|
||
## 分析记录
|
||
|
||
---
|
||
|
||
### 分析 #001
|
||
**时间**: 2026-03-29T19:16:18.924521
|
||
|
||
#### 系统提示词
|
||
```
|
||
你是一位专业的因果推断分析师。你的任务是分析给定的数据，识别因果变量，并对每个变量进行时间层级解析。
|
||
|
||
请以 JSON 格式输出分析结果，不要包含任何额外的解释或思考过程。
|
||
|
||
JSON 输出规范：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"变量名1": 整数层级,
|
||
"变量名2": 整数层级,
|
||
...
|
||
}
|
||
}
|
||
|
||
time_tiers 层级说明（整数，越小表示越早发生）：
|
||
- -1: 非时间变量（如样本唯一标识符 id）
|
||
- 0: 人口学特征或不变的混杂因素（如 age, gender, race）
|
||
- 1: 基线测量（干预前测得，可能是混杂因素，如 base_health）
|
||
- 2: 干预点/处理变量（如 treatment）
|
||
- 3: 中介变量（干预后、结果前测得）
|
||
- 4: 随访结果/结果变量（如 health）
|
||
- 5+: 更晚的时间点（如有多次随访）
|
||
|
||
注意：
|
||
- 只输出上述 JSON 格式，不要包含其他字段
|
||
- 处理变量和结果变量名称必须与数据表格的列名完全一致
|
||
- time_tiers 必须包含数据中的所有列名
|
||
- 不要使用 markdown 代码块标记（如 ```json）
|
||
- 直接输出纯 JSON 字符串
|
||
```
|
||
|
||
#### 用户提示词
|
||
```
|
||
请分析以下医疗数据，并严格按照 JSON 格式输出分析结果：
|
||
|
||
**数据列说明：**
|
||
- `id`: 样本唯一标识符
|
||
- `treatment`: 是否吃药（0=未吃药，1=吃药）
|
||
- `health`: 病人健康状态（0~1 浮点数，越高越好）
|
||
- `base_health`: 基准健康状态（未吃药时的健康状态）
|
||
- `age`: 年龄（18~70 岁）
|
||
|
||
|
||
**数据概览:**
|
||
- 样本数量：500
|
||
- 变量：id, treatment, health, base_health, age
|
||
|
||
**统计摘要:**
|
||
id treatment health base_health age
|
||
count 500.000000 500.000000 500.000000 500.000000 500.000000
|
||
mean 250.500000 0.656000 0.588928 0.414174 44.732000
|
||
std 144.481833 0.475517 0.211767 0.175689 15.239707
|
||
min 1.000000 0.000000 0.022200 0.012700 18.000000
|
||
25% 125.750000 0.000000 0.436300 0.282500 32.000000
|
||
50% 250.500000 1.000000 0.585800 0.407950 45.000000
|
||
75% 375.250000 1.000000 0.741575 0.534325 57.000000
|
||
max 500.000000 1.000000 1.000000 0.902500 70.000000
|
||
|
||
**处理变量分布:**
|
||
treatment
|
||
1 328
|
||
0 172
|
||
|
||
|
||
JSON 输出格式要求：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"treatment": 2,
|
||
"health": 4,
|
||
"base_health": 1,
|
||
"age": 0
|
||
}
|
||
}
|
||
|
||
要求：
|
||
1. 处理变量和结果变量名称必须与表格列名完全一致
|
||
2. time_tiers 必须覆盖所有列名
|
||
3. 只输出 JSON，不要包含其他任何内容
|
||
4. 不要使用 markdown 代码块标记
|
||
```
|
||
|
||
#### LLM 输出
|
||
```
|
||
|
||
|
||
{
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"treatment": 2,
|
||
"health": 4,
|
||
"base_health": 1,
|
||
"age": 0
|
||
}
|
||
}
|
||
```
|
||
|
||
#### 分析报告
|
||
```json
|
||
{
|
||
"query_interpretation": {
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"estimand": "ATE"
|
||
},
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"95%_CI": [
|
||
0.2347,
|
||
0.2539
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受治疗使患者随访时的 health 平均变化 0.2435 分（95%CI: 0.23-0.25）。"
|
||
},
|
||
"diagnostics": {
|
||
"balance_check": {
|
||
"age": {
|
||
"before": 0.4104,
|
||
"after": 0.0087
|
||
},
|
||
"base_health": {
|
||
"before": -0.3377,
|
||
"after": -0.0587
|
||
}
|
||
},
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"warnings": [
|
||
{
|
||
"type": "unobserved_confounding",
|
||
"message": "可能存在未观测混杂（如患者依从性、社会经济地位），建议进行敏感性分析。"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
#### 调用参数
|
||
```json
|
||
{
|
||
"data_path": "examples/medical_v2/data.xlsx",
|
||
"sample_size": 500,
|
||
"variables": [
|
||
"id",
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"treatment_variable": "treatment",
|
||
"outcome_variable": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"treatment": 2,
|
||
"health": 4,
|
||
"base_health": 1,
|
||
"age": 0
|
||
},
|
||
"llm_params": {
|
||
"base_url": "http://10.106.123.247:8000/v1",
|
||
"model": "qwen3.5-35b",
|
||
"temperature": 0.3,
|
||
"max_tokens": 2048
|
||
},
|
||
"candidates": [
|
||
{
|
||
"var": "base_health",
|
||
"pearson_T": -0.1612,
|
||
"pearson_Y": 0.7356,
|
||
"spearman_T": -0.1505,
|
||
"spearman_Y": 0.7267,
|
||
"pvalue_T": 0.0007,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0225,
|
||
"mi_Y": 1.413
|
||
},
|
||
{
|
||
"var": "age",
|
||
"pearson_T": 0.1913,
|
||
"pearson_Y": 0.2968,
|
||
"spearman_T": 0.1893,
|
||
"spearman_Y": 0.3077,
|
||
"pvalue_T": 0.0,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0152,
|
||
"mi_Y": 0.074
|
||
}
|
||
],
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"ATE_reported": 0.2435,
|
||
"95%_CI": [
|
||
0.2347,
|
||
0.2539
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受治疗使患者随访时的 health 平均变化 0.2435 分（95%CI: 0.23-0.25）。",
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"log_path": "examples/medical_v2/log.md"
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
|
||
---
|
||
|
||
### 分析 #002
|
||
**时间**: 2026-03-29T20:00:23.373499
|
||
|
||
#### 系统提示词
|
||
```
|
||
你是一位专业的因果推断分析师。你的任务是分析给定的数据，识别处理变量（treatment）、结果变量（outcome），并对每个变量进行时间层级解析。
|
||
|
||
请以 JSON 格式输出分析结果，不要包含任何额外的解释或思考过程。
|
||
|
||
JSON 输出规范：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"变量名1": 整数层级,
|
||
"变量名2": 整数层级,
|
||
...
|
||
}
|
||
}
|
||
|
||
time_tiers 层级说明（整数，越小表示越早发生）：
|
||
- -1: 非时间变量（如样本唯一标识符 id、index 等）
|
||
- 0: 人口学特征或不变的混杂因素（如 age、gender、region 等）
|
||
- 1: 基线测量（干预前测得，可能是混杂因素，如 baseline_score、pre_test 等）
|
||
- 2: 干预点/处理变量（如 treatment、intervention、policy 等）
|
||
- 3: 中介变量（干预后、结果前测得）
|
||
- 4: 随访结果/结果变量（如 outcome、post_test、score 等）
|
||
- 5+: 更晚的时间点（如有多次随访）
|
||
|
||
注意：
|
||
- 只输出上述 JSON 格式，不要包含其他字段
|
||
- treatment 和 outcome 必须是数据表格中真实存在的列名
|
||
- time_tiers 必须包含数据中的所有列名
|
||
- 不要使用 markdown 代码块标记（如 ```json）
|
||
- 直接输出纯 JSON 字符串
|
||
```
|
||
|
||
#### 用户提示词
|
||
```
|
||
请分析以下数据，并严格按照 JSON 格式输出分析结果：
|
||
|
||
|
||
**数据概览:**
|
||
- 样本数量：500
|
||
- 变量：id, treatment, health, base_health, age
|
||
|
||
**统计摘要:**
|
||
id treatment health base_health age
|
||
count 500.000000 500.000000 500.000000 500.000000 500.000000
|
||
mean 250.500000 0.656000 0.588928 0.414174 44.732000
|
||
std 144.481833 0.475517 0.211767 0.175689 15.239707
|
||
min 1.000000 0.000000 0.022200 0.012700 18.000000
|
||
25% 125.750000 0.000000 0.436300 0.282500 32.000000
|
||
50% 250.500000 1.000000 0.585800 0.407950 45.000000
|
||
75% 375.250000 1.000000 0.741575 0.534325 57.000000
|
||
max 500.000000 1.000000 1.000000 0.902500 70.000000
|
||
|
||
**处理变量分布:**
|
||
treatment
|
||
1 328
|
||
0 172
|
||
|
||
|
||
JSON 输出格式要求：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"列名1": 层级整数,
|
||
"列名2": 层级整数,
|
||
...
|
||
}
|
||
}
|
||
|
||
要求：
|
||
1. treatment 和 outcome 必须与表格列名完全一致
|
||
2. time_tiers 必须覆盖所有列名
|
||
3. 根据列名含义和统计摘要推断每个变量的时间层级
|
||
4. 只输出 JSON，不要包含其他任何内容
|
||
5. 不要使用 markdown 代码块标记
|
||
```
|
||
|
||
#### LLM 输出
|
||
```
|
||
|
||
|
||
{
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"age": 0,
|
||
"base_health": 1,
|
||
"treatment": 2,
|
||
"health": 4
|
||
}
|
||
}
|
||
```
|
||
|
||
#### 分析报告
|
||
```json
|
||
{
|
||
"query_interpretation": {
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"estimand": "ATE"
|
||
},
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"95%_CI": [
|
||
0.2349,
|
||
0.2535
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受治疗使患者随访时的 health 平均变化 0.2435 分（95%CI: 0.23-0.25）。"
|
||
},
|
||
"diagnostics": {
|
||
"balance_check": {
|
||
"age": {
|
||
"before": 0.4104,
|
||
"after": 0.0087
|
||
},
|
||
"base_health": {
|
||
"before": -0.3377,
|
||
"after": -0.0587
|
||
}
|
||
},
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"warnings": [
|
||
{
|
||
"type": "unobserved_confounding",
|
||
"message": "可能存在未观测混杂（如患者依从性、社会经济地位），建议进行敏感性分析。"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
#### 调用参数
|
||
```json
|
||
{
|
||
"data_path": "examples/medical_v2/data.xlsx",
|
||
"sample_size": 500,
|
||
"variables": [
|
||
"id",
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"treatment_variable": "treatment",
|
||
"outcome_variable": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"age": 0,
|
||
"base_health": 1,
|
||
"treatment": 2,
|
||
"health": 4
|
||
},
|
||
"llm_params": {
|
||
"base_url": "http://10.106.123.247:8000/v1",
|
||
"model": "qwen3.5-35b",
|
||
"temperature": 0.3,
|
||
"max_tokens": 2048
|
||
},
|
||
"candidates": [
|
||
{
|
||
"var": "base_health",
|
||
"pearson_T": -0.1612,
|
||
"pearson_Y": 0.7356,
|
||
"spearman_T": -0.1505,
|
||
"spearman_Y": 0.7267,
|
||
"pvalue_T": 0.0007,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0225,
|
||
"mi_Y": 1.413
|
||
},
|
||
{
|
||
"var": "age",
|
||
"pearson_T": 0.1913,
|
||
"pearson_Y": 0.2968,
|
||
"spearman_T": 0.1893,
|
||
"spearman_Y": 0.3077,
|
||
"pvalue_T": 0.0,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0152,
|
||
"mi_Y": 0.074
|
||
}
|
||
],
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"ATE_reported": 0.2435,
|
||
"95%_CI": [
|
||
0.2349,
|
||
0.2535
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受治疗使患者随访时的 health 平均变化 0.2435 分（95%CI: 0.23-0.25）。",
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"log_path": "examples/medical_v2/log.md"
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
|
||
---
|
||
|
||
### 分析 #003
|
||
**时间**: 2026-03-29T22:01:34.463873
|
||
|
||
#### 系统提示词
|
||
```
|
||
你是一位专业的因果推断分析师。你的任务是分析给定的数据，识别处理变量（treatment）、结果变量（outcome），并对每个变量进行时间层级解析。
|
||
|
||
请以 JSON 格式输出分析结果，不要包含任何额外的解释或思考过程。
|
||
|
||
JSON 输出规范：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"变量名1": 整数层级,
|
||
"变量名2": 整数层级,
|
||
...
|
||
}
|
||
}
|
||
|
||
time_tiers 层级说明（整数，越小表示越早发生）：
|
||
- -1: 非时间变量（如样本唯一标识符 id、index 等）
|
||
- 0: 人口学特征或不变的混杂因素（如 age、gender、region 等）
|
||
- 1: 基线测量（干预前测得，可能是混杂因素，如 baseline_score、pre_test 等）
|
||
- 2: 干预点/处理变量（如 treatment、intervention、policy 等）
|
||
- 3: 中介变量（干预后、结果前测得）
|
||
- 4: 随访结果/结果变量（如 outcome、post_test、score 等）
|
||
- 5+: 更晚的时间点（如有多次随访）
|
||
|
||
注意：
|
||
- 只输出上述 JSON 格式，不要包含其他字段
|
||
- treatment 和 outcome 必须是数据表格中真实存在的列名
|
||
- time_tiers 必须包含数据中的所有列名
|
||
- 不要使用 markdown 代码块标记（如 ```json）
|
||
- 直接输出纯 JSON 字符串
|
||
```
|
||
|
||
#### 用户提示词
|
||
```
|
||
请分析以下数据，并严格按照 JSON 格式输出分析结果：
|
||
|
||
**数据概览:**
|
||
- 样本数量：500
|
||
- 变量：id, treatment, health, base_health, age
|
||
|
||
**统计摘要:**
|
||
id treatment health base_health age
|
||
count 500.000000 500.000000 500.000000 500.000000 500.000000
|
||
mean 250.500000 0.656000 0.588928 0.414174 44.732000
|
||
std 144.481833 0.475517 0.211767 0.175689 15.239707
|
||
min 1.000000 0.000000 0.022200 0.012700 18.000000
|
||
25% 125.750000 0.000000 0.436300 0.282500 32.000000
|
||
50% 250.500000 1.000000 0.585800 0.407950 45.000000
|
||
75% 375.250000 1.000000 0.741575 0.534325 57.000000
|
||
max 500.000000 1.000000 1.000000 0.902500 70.000000
|
||
|
||
JSON 输出格式要求：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"列名1": 层级整数,
|
||
"列名2": 层级整数,
|
||
...
|
||
}
|
||
}
|
||
|
||
要求：
|
||
1. treatment 和 outcome 必须与表格列名完全一致
|
||
2. time_tiers 必须覆盖所有列名
|
||
3. 根据列名含义和统计摘要推断每个变量的时间层级
|
||
4. 只输出 JSON，不要包含其他任何内容
|
||
5. 不要使用 markdown 代码块标记
|
||
```
|
||
|
||
#### LLM 输出
|
||
```
|
||
{'treatment': 'treatment', 'outcome': 'health', 'time_tiers': {'id': -1, 'treatment': 2, 'health': 4, 'base_health': 1, 'age': 0}}
|
||
```
|
||
|
||
#### 分析报告
|
||
```json
|
||
{
|
||
"query_interpretation": {
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"estimand": "ATE"
|
||
},
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"95%_CI": [
|
||
0.2351,
|
||
0.2544
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受处理使 health 平均变化 0.2435 （95%CI: 0.24-0.25）。"
|
||
},
|
||
"diagnostics": {
|
||
"balance_check": {
|
||
"age": {
|
||
"before": 0.4104,
|
||
"after": 0.0087
|
||
},
|
||
"base_health": {
|
||
"before": -0.3377,
|
||
"after": -0.0587
|
||
}
|
||
},
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"warnings": [
|
||
{
|
||
"type": "unobserved_confounding",
|
||
"message": "可能存在未观测混杂，建议进行敏感性分析。"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
#### 调用参数
|
||
```json
|
||
{
|
||
"data_path": "examples/medical_v2/data.xlsx",
|
||
"sample_size": 500,
|
||
"variables": [
|
||
"id",
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"treatment_variable": "treatment",
|
||
"outcome_variable": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"treatment": 2,
|
||
"health": 4,
|
||
"base_health": 1,
|
||
"age": 0
|
||
},
|
||
"llm_params": {
|
||
"base_url": "http://10.106.123.247:8000/v1",
|
||
"model": "qwen3.5-35b",
|
||
"temperature": 0.3,
|
||
"max_tokens": 2048
|
||
},
|
||
"candidates": [
|
||
{
|
||
"var": "base_health",
|
||
"pearson_T": -0.1612,
|
||
"pearson_Y": 0.7356,
|
||
"spearman_T": -0.1505,
|
||
"spearman_Y": 0.7267,
|
||
"pvalue_T": 0.0007,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0225,
|
||
"mi_Y": 1.413
|
||
},
|
||
{
|
||
"var": "age",
|
||
"pearson_T": 0.1913,
|
||
"pearson_Y": 0.2968,
|
||
"spearman_T": 0.1893,
|
||
"spearman_Y": 0.3077,
|
||
"pvalue_T": 0.0,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0152,
|
||
"mi_Y": 0.074
|
||
}
|
||
],
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"ATE_reported": 0.2435,
|
||
"95%_CI": [
|
||
0.2351,
|
||
0.2544
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受处理使 health 平均变化 0.2435 （95%CI: 0.24-0.25）。",
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"log_path": "examples/medical_v2/log.md"
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
|
||
---
|
||
|
||
### 分析 #004
|
||
**时间**: 2026-03-29T22:05:26.081133
|
||
|
||
#### 系统提示词
|
||
```
|
||
你是一位专业的因果推断分析师。你的任务是分析给定的数据，识别处理变量（treatment）、结果变量（outcome），并对每个变量进行时间层级解析。
|
||
|
||
请以 JSON 格式输出分析结果，不要包含任何额外的解释或思考过程。
|
||
|
||
JSON 输出规范：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"变量名1": 整数层级,
|
||
"变量名2": 整数层级,
|
||
...
|
||
}
|
||
}
|
||
|
||
time_tiers 层级说明（整数，越小表示越早发生）：
|
||
- -1: 非时间变量（如样本唯一标识符 id、index 等）
|
||
- 0: 人口学特征或不变的混杂因素（如 age、gender、region 等）
|
||
- 1: 基线测量（干预前测得，可能是混杂因素，如 baseline_score、pre_test 等）
|
||
- 2: 干预点/处理变量（如 treatment、intervention、policy 等）
|
||
- 3: 中介变量（干预后、结果前测得）
|
||
- 4: 随访结果/结果变量（如 outcome、post_test、score 等）
|
||
- 5+: 更晚的时间点（如有多次随访）
|
||
|
||
注意：
|
||
- 只输出上述 JSON 格式，不要包含其他字段
|
||
- treatment 和 outcome 必须是数据表格中真实存在的列名
|
||
- time_tiers 必须包含数据中的所有列名
|
||
- 不要使用 markdown 代码块标记（如 ```json）
|
||
- 直接输出纯 JSON 字符串
|
||
```
|
||
|
||
#### 用户提示词
|
||
```
|
||
请分析以下数据，并严格按照 JSON 格式输出分析结果：
|
||
|
||
**数据概览:**
|
||
- 样本数量：500
|
||
- 变量：id, treatment, health, base_health, age
|
||
|
||
**统计摘要:**
|
||
id treatment health base_health age
|
||
count 500.000000 500.000000 500.000000 500.000000 500.000000
|
||
mean 250.500000 0.656000 0.588928 0.414174 44.732000
|
||
std 144.481833 0.475517 0.211767 0.175689 15.239707
|
||
min 1.000000 0.000000 0.022200 0.012700 18.000000
|
||
25% 125.750000 0.000000 0.436300 0.282500 32.000000
|
||
50% 250.500000 1.000000 0.585800 0.407950 45.000000
|
||
75% 375.250000 1.000000 0.741575 0.534325 57.000000
|
||
max 500.000000 1.000000 1.000000 0.902500 70.000000
|
||
|
||
JSON 输出格式要求：
|
||
{
|
||
"treatment": "处理变量名称",
|
||
"outcome": "结果变量名称",
|
||
"time_tiers": {
|
||
"列名1": 层级整数,
|
||
"列名2": 层级整数,
|
||
...
|
||
}
|
||
}
|
||
|
||
要求：
|
||
1. treatment 和 outcome 必须与表格列名完全一致
|
||
2. time_tiers 必须覆盖所有列名
|
||
3. 根据列名含义和统计摘要推断每个变量的时间层级
|
||
4. 只输出 JSON，不要包含其他任何内容
|
||
5. 不要使用 markdown 代码块标记
|
||
```
|
||
|
||
#### LLM 输出
|
||
```
|
||
{'treatment': 'treatment', 'outcome': 'health', 'time_tiers': {'id': -1, 'treatment': 2, 'health': 4, 'base_health': 1, 'age': 0}}
|
||
```
|
||
|
||
#### 分析报告
|
||
```json
|
||
{
|
||
"query_interpretation": {
|
||
"treatment": "treatment",
|
||
"outcome": "health",
|
||
"estimand": "ATE"
|
||
},
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"95%_CI": [
|
||
0.2356,
|
||
0.254
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受处理使 health 平均变化 0.2435 （95%CI: 0.24-0.25）。"
|
||
},
|
||
"diagnostics": {
|
||
"balance_check": {
|
||
"age": {
|
||
"before": 0.4104,
|
||
"after": 0.0087
|
||
},
|
||
"base_health": {
|
||
"before": -0.3377,
|
||
"after": -0.0587
|
||
}
|
||
},
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"warnings": [
|
||
{
|
||
"type": "unobserved_confounding",
|
||
"message": "可能存在未观测混杂，建议进行敏感性分析。"
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
#### 调用参数
|
||
```json
|
||
{
|
||
"data_path": "examples/medical_v2/data.xlsx",
|
||
"sample_size": 500,
|
||
"variables": [
|
||
"id",
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"treatment_variable": "treatment",
|
||
"outcome_variable": "health",
|
||
"time_tiers": {
|
||
"id": -1,
|
||
"treatment": 2,
|
||
"health": 4,
|
||
"base_health": 1,
|
||
"age": 0
|
||
},
|
||
"llm_params": {
|
||
"base_url": "http://10.106.123.247:8000/v1",
|
||
"model": "qwen3.5-35b",
|
||
"temperature": 0.3,
|
||
"max_tokens": 2048
|
||
},
|
||
"candidates": [
|
||
{
|
||
"var": "base_health",
|
||
"pearson_T": -0.1612,
|
||
"pearson_Y": 0.7356,
|
||
"spearman_T": -0.1505,
|
||
"spearman_Y": 0.7267,
|
||
"pvalue_T": 0.0007,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0225,
|
||
"mi_Y": 1.413
|
||
},
|
||
{
|
||
"var": "age",
|
||
"pearson_T": 0.1913,
|
||
"pearson_Y": 0.2968,
|
||
"spearman_T": 0.1893,
|
||
"spearman_Y": 0.3077,
|
||
"pvalue_T": 0.0,
|
||
"pvalue_Y": 0.0,
|
||
"mi_T": 0.0152,
|
||
"mi_Y": 0.074
|
||
}
|
||
],
|
||
"causal_graph": {
|
||
"nodes": [
|
||
"treatment",
|
||
"health",
|
||
"base_health",
|
||
"age"
|
||
],
|
||
"edges": [
|
||
{
|
||
"from": "treatment",
|
||
"to": "health",
|
||
"type": "hypothesized"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "base_health",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "treatment",
|
||
"type": "confounding"
|
||
},
|
||
{
|
||
"from": "age",
|
||
"to": "health",
|
||
"type": "confounding"
|
||
}
|
||
],
|
||
"backdoor_paths": [
|
||
"treatment <- base_health -> health",
|
||
"treatment <- age -> health"
|
||
]
|
||
},
|
||
"identification": {
|
||
"strategy": "Backdoor Adjustment",
|
||
"adjustment_set": [
|
||
"age",
|
||
"base_health"
|
||
],
|
||
"reasoning": "发现 2 条后门路径。通过控制变量 ['age', 'base_health'] 可阻断所有后门路径，满足后门准则。"
|
||
},
|
||
"estimation": {
|
||
"ATE_Outcome_Regression": 0.2444,
|
||
"ATE_IPW": 0.2425,
|
||
"ATE_reported": 0.2435,
|
||
"95%_CI": [
|
||
0.2356,
|
||
0.254
|
||
],
|
||
"interpretation": "在控制 ['age', 'base_health'] 后，接受处理使 health 平均变化 0.2435 （95%CI: 0.24-0.25）。",
|
||
"overlap_assumption": "满足",
|
||
"robustness": "稳健"
|
||
},
|
||
"log_path": "examples/medical_v2/log.md"
|
||
}
|
||
```
|
||
|
||
---
|
||
|