Amalgamating Evidence About Causes: Medicine, the Medical Sciences, and Beyond

在许多科学领域，当推断因果关系时，可以依靠来自不同方法、专家和学科的各种证据。对科学家和那些希望在决策中依赖因果主张的人来说，合并证据以产生因果知识是一个广泛的挑战。这在生物医学科学和医学实践中非常重要。在医学中，至少有四个领域的从业者需要合并因果知识：在常规临床实践中治疗一系列患者，测量多个医学试验的效应大小并将其汇总为总体效应大小，根据不同的证据对干预效果进行推断，以及合并一组专家的判断。在每个领域中，与假定的因果关系有关的证据具有不同的形式和性质以及不同的可靠性，并且不同的证据可以合并的方式在不同的领域中也各不相同。这个项目的主要目的是利用科学哲学的工具来评估医学中因果证据的合并。证据融合最近在科学哲学中受到了一些关注（参见Fletcher， Landes & Poellinger 2019年的总体概述）。对于证据合并问题，一种有影响力的哲学方法建立在Bovens和Hartmann（2003）开发的贝叶斯网络框架的基础上（Menon和Stegenga 2017； Landes， Osimani和Poellinger 2018）。另一种方法以著名的阿罗不可能性定理为出发点，提出证据的合并是否面临与偏好合并相似的约束（Stegenga 2013; Cresto & Tajer 2020）。Bradley, Dietrich, & List（2014）使用判断聚合工作的结果来阐明因果判断合并的约束。科学哲学中证据合并的另一种途径是阐明科学实践中证据合并的方法论问题。

In many areas of science, a variety of evidence from different methods, experts, and disciplines can be relied on when inferring causal claims. The amalgamation of evidence to produce causal knowledge is a widespread challenge for scientists and those aiming to rely on causal claims in decision-making. This is acutely important in the biomedical sciences and in medical practice. In medicine there are at least four domains in which practitioners are required to amalgamate causal knowledge: treating a sequence of patients in routine clinical practice, measuring effect sizes from multiple medical trials and aggregating them into an overall effect size, making inferences about intervention effects based on diverse evidence, and amalgamating a group of experts' judgements. In each domain the evidence pertaining to the putative causal relations has distinct forms and properties and varying reliability, and the ways in which that disparate evidence can be amalgamated itself varies between the domains. The broad aim of this project is to evaluate the amalgamation of causal evidence in medicine using tools from philosophy of science. Amalgamation of evidence has received some recent attention in philosophy of science (see Fletcher, Landes & Poellinger 2019 for a general overview). One influential philosophical approach to the question of evidence amalgamation builds off the Bayesian network framework developed in Bovens & Hartmann (2003) (Menon & Stegenga 2017; Landes, Osimani & Poellinger 2018). Another approach takes as its starting point the famous Arrow impossibility theorem, asking if the amalgamation of evidence faces similar constraints as the amalgamation of preferences (Stegenga 2013; Cresto & Tajer 2020). Bradley, Dietrich, & List (2014) use results from work on judgement aggregation to articulate constraints on the amalgamation of causal judgements. Still another approach to evidence amalgamation in philosophy of science is to articulate methodological problems of evidence amalgamation in scientific practice.