A probability index of the robustness of a causal inference

被引:15
|
作者
Pan, W
Frank, KA
机构
[1] Univ Cincinnati, Div Educ Studies, Cincinnati, OH 45221 USA
[2] Michigan State Univ, Dept Counseling Educ Psychol & Special Educ, E Lansing, MI 48824 USA
关键词
causal inference; confounding variables; linear models; regression coefficients; sensitivity analysis;
D O I
10.3102/10769986028004315
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Causal inference is an important, controversial topic in the social sciences, where it is difficult to conduct experiments or measure and control for all confounding variables. To address this concern, the present study presents a probability index to assess the robustness of a causal inference to the impact of a confounding variable. The information from existing covariates is used to develop a reference distribution for gauging the likelihood of observing a given value of the impact of a confounding variable. Applications are illustrated with an empirical example pertaining to educational attainment. The methodology discussed in this study allows for multiple partial causes in the complex social phenomena that we study, and informs the controversy about causal inference that arises from the use of statistical models in the social sciences.
引用
收藏
页码:315 / 337
页数:23
相关论文
共 50 条
  • [41] Causal Inference by Compression
    Budhathoki, Kailash
    Vreeken, Jilles
    [J]. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2016, : 41 - 50
  • [42] Improving causal inference
    Ebrahim, Shah
    [J]. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2013, 42 (02) : 363 - 366
  • [43] Ancestral Causal Inference
    Magliacane, Sara
    Claassen, Tom
    Mooij, Joris M.
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
  • [44] Interventions and causal inference
    Eberhardt, Frederick
    Scheines, Richard
    [J]. PHILOSOPHY OF SCIENCE, 2007, 74 (05) : 981 - 995
  • [45] Causal dynamic inference
    Alexander Bochman
    Dov M. Gabbay
    [J]. Annals of Mathematics and Artificial Intelligence, 2012, 66 : 231 - 256
  • [46] Causal inference for clinicians
    Stovitz, Steven D.
    Shrier, Ian
    [J]. BMJ EVIDENCE-BASED MEDICINE, 2019, 24 (03) : 109 - 112
  • [47] RESTRICTED CAUSAL INFERENCE
    KLOPOTEK, MA
    [J]. INFORMATION PROCESSING '94, VOL I: TECHNOLOGY AND FOUNDATIONS, 1994, 51 : 342 - 347
  • [48] Polydesigns and causal inference
    Li, Fan
    Frangakis, Constantine E.
    [J]. BIOMETRICS, 2006, 62 (02) : 343 - 351
  • [49] Causal inference in cplint
    Riguzzi, Fabrizio
    Cota, Giuseppe
    Bellodi, Elena
    Zese, Riccardo
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2017, 91 : 216 - 232
  • [50] Entropic Causal Inference
    Kocaoglu, Murat
    Dimakis, Alexandros G.
    Vishwanath, Sriram
    Hassibi, Babak
    [J]. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 1156 - 1162