因果推斷三種分析框架及其應用綜述

馬忠貴; 徐曉晗; 劉雪兒

doi:10.13374/j.issn2095-9389.2021.07.04.002

因果推斷三種分析框架及其應用綜述

doi: 10.13374/j.issn2095-9389.2021.07.04.002

北京科技大學計算機與通信工程學院，北京 100083

基金項目: 中央高校基本科研業務費專項資金資助項目（FRF-DF-20-12, FRF-GF-18-017B）

詳細信息

通訊作者:
E-mail: g_runeko@163.com

中圖分類號: TG142.71
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-07-04
- 網絡出版日期: 2021-10-19
- 刊出日期: 2022-07-01

Three analytical frameworks of causal inference and their applications

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: g_runeko@163.com

摘要

摘要: 介紹因果推斷所涉及的基本概念及其三種分析框架：反事實框架、潛在結果模型和結構因果模型。首先，從反事實框架介紹因果效應的發端；然后，從基于反事實的兩個因果推斷分析框架：潛在結果模型和結構因果模型，來分別闡述兩個分析框架所涉及的關鍵理論和應用方法。其中，潛在結果模型使用數學和可計算的語言對因果理論進行闡述，是一種將假設、命題和結論清晰化表達的計算模型，其在原因和結果變量已知的前提下定量分析原因變量對結果變量的因果效應，并對缺失的潛在結果進行補齊，使觀察性研究的效果接近試驗性研究。結構因果模型則是一種基于圖論的因果推斷方法，它將事件分為觀察、干預和反事實三個層級，并通過do運算將干預和反事實層級的因果關系都降維成可以通過統計學手段解決的問題。最后，探討了現今多領域內因果推斷的應用場景，并總結了三種分析框架的異同點。
- 因果效應 /
- 因果推斷 /
- 反事實 /
- 潛在結果模型 /
- 結構因果模型
Abstract: Causality is a generic relationship between an effect and a cause that produces it. The causal relationship among things has been a research hotspot; however, the complexity of causality is sometimes far beyond our imagination. Although some causality problems seem easy to analyze, finding an exact answer may not be easy. Nevertheless, through the continuous innovation and development of empirical research methods in recent decades, we have had several clear analytical frameworks and effective methods on how to define and estimate causality. Exploring the causal effects among things is a promising research topic in many fields, such as statistics, computer science, and econometrics. With Joshua D. Angrist and Guido W. Imbens winning the Nobel Prize in economics for their methodological contributions to the analysis of causality in 2021, causal inference is expected to thrive in these fields. This paper briefly introduces the basic concepts involved in causal inference and its three analytical frameworks, namely, counterfactual framework (CF), potential outcome framework (POF), and structural causal model (SCM). Firstly, we introduce the origin of causal effects according to CF. Secondly, based on the counterfactual theory, two analysis frameworks are considered (POF and SCM), and we introduce the associated key theories and methods. The SCM explains the causal theory through mathematics and computable language, and it is a calculation model that clearly expresses hypotheses, propositions, and conclusions. It quantitatively analyzes the pair of cause variables under the premise that the cause and effect variables are known. The POF makes up for the missing potential results, such that the effect of the observational research is close to experimental research. The SCM is a causal inference method based on graph theory. It divides events into three levels: observation, intervention, and counterfactual. Through the “do” operation, the causal relationship at the intervention and counterfactual levels could be reduced to low-dimensional problems, which can be solved via statistical methods. Finally, the current application scenarios of causal inference in many fields are discussed in this paper, and the three analysis frameworks are compared.
- causal effect /
- causal inference /
- counterfactual /
- potential outcome model /
- structural causality model

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