2024 11 v.39;No.290 3-17
网络观测数据下考虑邻域处理的因果推断模型研究
基金项目(Foundation):
国家社会科学基金一般项目“大规模面板计数数据的高维协变量的稳健降维和应用研究”(22BTJ069)
邮箱(Email):
DOI:
10.20207/j.cnki.1007-3116.2024.0016
中文作者单位:
浙江财经大学数据科学学院;
摘要(Abstract):
因果效应评估是理解变量之间相互作用及因果机制的理论基础，然而在社交网络中进行因果效应评估一直是一个备受挑战的研究领域。因为网络中个体间的相互影响会使得传统的因果推断方法变得具有挑战性。文献中考虑邻域处理产生网络效应的研究较少，而且仅仅考虑二元处理下的因果推断，未能充分应对多重水平的处理情境。但现实观察性研究中普遍存在多重水平处理。文章提出了网络观测数据下考虑邻域处理及其影响的策略评估的模型和方法。首先，放松SUTVA条件，提出考虑邻域处理影响的个体处理稳定性假设。基于此构建适用于多重水平处理和邻域处理效应的因果推断理论框架。其次，改进原先使用倾向得分进行因果推断的模型，提出包含邻域处理的广义倾向得分。最后，通过数值模拟和理论分析，研究不同处理分配机制对个体结果的影响，并证明了该模型的稳定性和有效性。该模型为网络数据中的因果推断提供了一种有效方法，并具有广泛的应用前景和实际应用价值。
关键词(KeyWords):
因果推断;;网络效应;;广义倾向得分;;多重水平处理;;政策评估
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[2] ATHEY S,ECKLES D,IMBENS G W.Exact p-values for network interference[J].Journal of the American statistical association,2018,113(521):230-240.
[3] RUBIN D B.Estimating causal effects of treatments in randomized and nonrandomized studies[J].Journal of educational psychology,1974,66(5):688-701.
[4] PEARL J.Causal diagrams for empirical research:rejoinder to discussions of causal diagrams for empirical research[J].Biometrika,1995,82(4):694-695.
[5] SOBEL M E.What do randomized studies of housing mobility demonstrate?Causal inference in the face of interference[J].Journal of the American statistical association,2006,101(476):1398-1407.
[6] COX D R.Planning of experiments[M].New York:Wiley,1958:76-80.
[7] SOBEL M E.An introduction to causal inference[J].Sociological methods & research,1996,24(3):353-379.
[8] TOULIS P,KAO E.Estimation of causal peer influence effects[C].International conference on machine learning.PMLR,2013:1489-1497.
[9] BANG H,ROBINS J M.Doubly robust estimation in missing data and causal inference models[J].Biometrics,2005,61(4):962-973.
[10] ROSENBAUM P R,RUBIN D B.The central role of the propensity score in observational studies for causal effects[J].Biometrika,1983,70(1):41-55.
[11] HECKMAN J J,ROBB R Jr.Alternative methods for evaluating the impact of interventions:an overview[J].Journal of econometrics,1985,30(1/2):239-267.
[12] IMBENS G W.The role of the propensity score in estimating dose-response functions[J].Biometrika,2000,87(3):706-710.
[13] HIRANO K,IMBENS G W.The propensity score with continuous treatments[M].New York:Wiley-Blackwell,2004:73-84.
[14] 兰秀娟，张卫国.高铁网络影响下城市群经济联系格局与区域经济协调发展[J].统计与信息论坛，2023,38(10):21-34.
[15] 李爽，刘静静，安康.基于线上数据的中国省域旅游流网络结构特征与空间效应研究[J].统计与信息论坛，2023,38(1):116-128.
[16] LIU L,HUDGENS M G BECKER-DREPS S.On inverse probability-weighted estimators in the presence of interference[J].Biometrika,2016,103(4):829-842.
[17] FORASTIERE L,AIROLDI E M,MEALLI F.Identification and estimation of treatment and interference effects in observational studies on networks[J].Journal of the american statistical association,2021,116(534):901-918.
[18] LIU J,YE F,YANG Y.Nonparametric doubly robust estimation of causal effect on networks in observational studies[J].Stat,2023,12(1):1-14.
[19] LEUNG M P.Causal inference under approximate neighborhood interference[J].Econometrica,2022,90(1):267-293.
[20] OGBURN E L,SOFRYGIN O,DIAZ I,et al.Causal inference for social network data[J].Journal of the American statistical association,2024,119(545):597-611.
[21] PIERONI V,LATTANZI N,RICCABONI M.The dynamic impact of inter-firm network agreements[J].Small business economics,2024,63(3):939-969.
[22] FORASTIERE L,MEALLI F,WU A,et al.Estimating causal effects under network interference with Bayesian generalized propensity scores[J].Journal of machine learning research,2022,23(289):1-61.
[23] LEE Y,Ogburn E L.Network dependence can lead to spurious associations and invalid inference[J].Journal of the american statistical association,2021,116(535):1060-1074.
[24] ROSENBAUM P R.Model-based direct adjustment[J].Journal of the american statistical association,1987,82(398):387-394.
[25] MCCAFFREY D F,GRIFFIN B A,ALMIRALL D,et al.A tutorial on propensity score estimation for multiple treatments using generalized boosted models[J].Statistics in medicine,2013,32(19):3388-3414.
[26] RUBIN D B.Comment:Neyman (1923) and causal inference in experiments and observational studies[J].Statistical science,1990,5(4):472-480.
[27] TCHETGEN E J T,VANDERWEELE T J.On causal inference in the presence of interference[J].Statistical methods in medical research,2012,21(1):55-75.
[28] BARTLETT M S.The use of transformations[J].Biometrics,1947,3(1):39-52.
[29] DUCHESNE P,DE MICHEAUX P L.Computing the distribution of quadratic forms:further comparisons between the Liu-Tang-Zhang approximation and exact methods[J].Computational statistics & data analysis,2010,54(4):858-862.
基本信息:
DOI：10.20207/j.cnki.1007-3116.2024.0016
中图分类号:O212.1
引用信息:
[1]赵晓兵,李洋阔,徐菁.网络观测数据下考虑邻域处理的因果推断模型研究[J].统计与信息论坛,2024,39(11):3-17.DOI:10.20207/j.cnki.1007-3116.2024.0016.
基金信息:
国家社会科学基金一般项目“大规模面板计数数据的高维协变量的稳健降维和应用研究”(22BTJ069)
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