Amalgamating Evidence About Causes: Medicine, the Medical Sciences, and Beyond
Lead Research Organisation:
University of Cambridge
Department Name: History and Philosophy Of Science
Abstract
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.
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.
