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格莱默是卡内基梅隆大学哲学系的创始人，古根海姆研究员（[[Guggenheim Fellowship|Guggenheim Fellow]]），行为科学高级研究中心研究员<ref>{{cite web|url=https://casbs.stanford.edu/news/awards-and-elections-fall-2019|title=Awards and Elections, Fall 2019|publisher=Center for Advanced Study in Behavioral Sciences|accessdate=December 16, 2019}}</ref>，[[Phi Beta Kappa Society|Phi Beta Kappa]]联谊会讲师<ref>{{cite web|url=https://www.pbk.org/Awards/Romanell/PastWinners|title=Romanell-Phi Beta Kappa Professorship Past Winners|publisher=Phi Beta Kappa|accessdate=December 16, 2019}}</ref>，美国科学促进会（AAAS）统计部门研究员<ref>{{cite web|url=https://www.amacad.org/person/clark-glymour|title=Clark Glymour|publisher=American Academy of Arts and Sciences|accessdate=December 16, 2019}}</ref>。格莱默和他的合作者创造了贝叶斯网络的因果解释<ref>P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993.</ref>。他的研究兴趣领域包括: 认识论（[[epistemology]]）<ref>Epistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, {{ISBN|87-92130-07-0}}.</ref>(尤其是 Android 认识论（[[Android epistemology]]）)、机器学习（[[machine learning]],）、自动推理（[[automated reasoning]]）、判断心理学（[[psychology]] of judgment）和数学心理学（[[mathematical psychology]]）。<ref>{{cite web|url=https://www.ihmc.us/groups/clark-glymour/|title=Clark Glymour|accessdate=December 16, 2019}}</ref>格莱莫尔对科学哲学的主要贡献之一是在贝叶斯概率（[[Bayesian probability]]）领域，特别是在他对贝叶斯“旧证据问题”的分析中<ref>{{cite web|url=http://plato.stanford.edu/entries/epistemology-bayesian/|title=Bayesian Epistemology|date=July 12, 2001}}</ref><ref>Glymour, C.; Theory and evidence (1981), pp. 63-93.</ref>。格莱默与彼得 · 斯皮尔茨（Peter Spirtes）和理查德 · 谢恩斯（Richard Scheines）合作，还开发了一种自动因果推理算法，以软件形式实现，命名为[[TETRAD]]<ref>[http://www.phil.cmu.edu/projects/tetrad/publications.html Publications] TETRAD. Retrieved December 16, 2019.</ref>。采用多元统计数据作为输入，TETRAD 从所有可能的因果关系模型中快速搜索，并根据这些变量之间的条件依赖关系输出最合理的因果模型。该算法基于统计学、图论、科学哲学和人工智能的原理<ref>Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. doi:[https://doi.org/10.1207%2Fs15327906mbr2302_13 10.1207/s15327906mbr2302_13]. [[wikipedia:PMID_(identifier)|PMID]] [https://pubmed.ncbi.nlm.nih.gov/26764954 26764954].</ref>。

格莱默是卡内基梅隆大学哲学系的创始人，古根海姆研究员（[[Guggenheim Fellowship|Guggenheim Fellow]]），行为科学高级研究中心研究员<ref>{{cite web|url=https://casbs.stanford.edu/news/awards-and-elections-fall-2019|title=Awards and Elections, Fall 2019|publisher=Center for Advanced Study in Behavioral Sciences|accessdate=December 16, 2019}}</ref>，[[Phi Beta Kappa Society|Phi Beta Kappa]]联谊会讲师<ref>{{cite web|url=https://www.pbk.org/Awards/Romanell/PastWinners|title=Romanell-Phi Beta Kappa Professorship Past Winners|publisher=Phi Beta Kappa|accessdate=December 16, 2019}}</ref>，美国科学促进会（AAAS）统计部门研究员<ref>{{cite web|url=https://www.amacad.org/person/clark-glymour|title=Clark Glymour|publisher=American Academy of Arts and Sciences|accessdate=December 16, 2019}}</ref>。格莱默和他的合作者创造了贝叶斯网络的因果解释<ref>P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993.</ref>。他的研究兴趣领域包括: 认识论（[[epistemology]]）<ref>Epistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, [[wikipedia:ISBN_(identifier)|ISBN]] [[wikipedia:Special:BookSources/87-92130-07-0|87-92130-07-0]]. </ref>(尤其是 Android 认识论（[[Android epistemology]]）)、机器学习（[[machine learning]],）、自动推理（[[automated reasoning]]）、判断心理学（[[psychology]] of judgment）和数学心理学（[[mathematical psychology]]）。<ref>{{cite web|url=https://www.ihmc.us/groups/clark-glymour/|title=Clark Glymour|accessdate=December 16, 2019}}</ref>格莱莫尔对科学哲学的主要贡献之一是在贝叶斯概率（[[Bayesian probability]]）领域，特别是在他对贝叶斯“旧证据问题”的分析中<ref>{{cite web|url=http://plato.stanford.edu/entries/epistemology-bayesian/|title=Bayesian Epistemology|date=July 12, 2001}}</ref><ref>Glymour, C.; Theory and evidence (1981), pp. 63-93.</ref>。格莱默与彼得 · 斯皮尔茨（Peter Spirtes）和理查德 · 谢恩斯（Richard Scheines）合作，还开发了一种自动因果推理算法，以软件形式实现，命名为[[TETRAD]]<ref>[http://www.phil.cmu.edu/projects/tetrad/publications.html Publications] TETRAD. Retrieved December 16, 2019.</ref>。采用多元统计数据作为输入，TETRAD 从所有可能的因果关系模型中快速搜索，并根据这些变量之间的条件依赖关系输出最合理的因果模型。该算法基于统计学、图论、科学哲学和人工智能的原理<ref>Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. doi:[https://doi.org/10.1207%2Fs15327906mbr2302_13 10.1207/s15327906mbr2302_13]. [[wikipedia:PMID_(identifier)|PMID]] [https://pubmed.ncbi.nlm.nih.gov/26764954 26764954].</ref>。