Fuller Jonathan
Faculty of Medicine, University of Toronto, Ontario, Canada.
African Centre for Epistemology and Philosophy of Science University of Johannesburg South Africa.
Br J Philos Sci. 2019 Sep;70(3):901-926. doi: 10.1093/bjps/axx015. Epub 2018 Jan 22.
It is sometimes thought that randomized study group allocation is uniquely proficient at producing comparison groups that are evenly balanced for all confounding causes. Philosophers have argued that in real randomized controlled trials this balance assumption typically fails. But is the balance assumption an important ideal? I run a thought experiment, the CONFOUND study, to answer this question. I then suggest a new account of causal inference in ideal and real comparative group studies that helps clarify the roles of confounding variables and randomization. 1Confounders and Causes2The Balance Assumption3The CONFOUND Study 3.1CONFOUND 13.2CONFOUND 24Disjunction C and the Ideal Study 4.1The ultimate 'other cause': C4.2The ideal comparative group study4.3Required conditions for causal inference5Confounders as Causes, Confounders as Correlates6Summary.
有时人们认为，随机分配研究组在产生对所有混杂因素均保持均衡的对照组方面具有独特的优势。哲学家们认为，在实际的随机对照试验中，这种均衡假设通常是不成立的。但这种均衡假设是一个重要的理想状态吗？我进行了一项思想实验，即“混杂因素研究”来回答这个问题。然后，我提出了一种关于理想和实际比较组研究中因果推断的新解释，这有助于阐明混杂变量和随机化的作用。1. 混杂因素与原因2. 均衡假设3. 混杂因素研究 3.1 混杂因素研究1 3.2 混杂因素研究24. 析取C与理想研究 4.1 最终的“其他原因”：C 4.2 理想的比较组研究4.3 因果推断的必要条件5. 作为原因的混杂因素，作为相关因素的混杂因素6. 总结
