Causal Counterfactual visualisation for human causal decision making - A case study in healthcare

人类因果决策的因果反事实可视化 - 医疗保健领域的案例研究

• 批准号: EP/X029778/1
• 负责人: Feng Dong
• 金额: $ 77.36万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX029778%2F1
• 关键词: Causal; Counterfactual; visualisation; human; causal

The concept of causation is central to our understanding of the world and key to human decision making. Causation is fundamentally different from association as it exhibits consequences of interventions. Evidence shows that different causal beliefs tend to result in different health outcomes. At present, psychology research still does not provide answers to many fundamental questions about how people make causal judgements in real-world decisions. According to recent psychological theories of causation, people's causal judgments are based on assessing whether (and how) an presumed cause makes a difference to an outcome. Crucially, these judgements hinge on counterfactual contrasts, namely what would have happened if the presumed cause had not occurred? This involves envisaging alternative possibilities to the actual world in mental simulations. To this end, visualisation has great potential to support people's counterfactual thinking, especially in the face of complex scenarios. However, so far very little work has addressed counterfactual visualisation in the context of aiding causal decision making.This research will investigate novel causal counterfactual visualisation, which will, in contrast to the direct visualisation of real data, have a new functionality to render causal counterfactuals that did not occur in reality. The counterfactuals will be generated by a counterfactual simulation model that is trained with real data. This extends standard data visualisation by visualising hypothetical exemplars beyond real data. It will support "explanation-with-examples" by enabling decision makers to interactively create synthetic data and examine "close possible worlds" (e.g. different outcomes from a small causal change). Visualising concrete exemplars will allow people to view key evidence and contest their decisions against the counterfactuals to gain actionable insights.Causal counterfactual visualisation will be underpinned by the latest advance in both psychology and AI domains made by the members of this team. By bringing together expertise from a multidisciplinary team, the new causal counterfactual visualisation techniques will offer an useful channel to assess and further our understanding of human behaviour and performance in causal decision making with the aid of visual presentation, especially with respect to the role of visualisation.We will conduct a series of psychological experiments in the context of clinical case studies to probe causal decision making in healthcare, which will involve participations and co-design with doctors. Doctors will use the visualisation tool to make and test causal hypotheses for decision making in the psychological experiments involving two clinical cases: (1) Clinical judgement based on causal risk factors of developing lung cancer, which targets unit-level causality about individual patients; (2) Clinical trial of a drug by measuring its causal effect on diabetes treatment, which targets causation at the population-level. With customised simulations, the visualisation will reveal potential outcomes of alternative treatments and their trade-off in efficacy & side-effects to enable comparisons by visualising concrete counterfactuals (e.g. medical tests, screening, images). Also, researchers can assess difference between treatment and control groups in a simulated clinical trial, and view examples that violate their hypotheses. Through the two clinical use cases, the research outcomes will be directly measured in the healthcare setting, where clinicians face complex landscape of medicine with overwhelming number of input and interplay of data from multiple sources to make important clinical decisions. This work is about to demonstrate how the new visualisation can seek to resolve this leading to reduction variability and support robust decision making with actionable insights that clinicians can interpret.

因果关系的概念是我们理解世界的核心，也是人类决策的关键。因果关系从根本上不同于关联，因为它展示了干预的后果。有证据表明，不同的因果信念往往会导致不同的健康结果。目前，心理学研究仍然没有提供关于人们如何在现实世界中做出因果判断的许多基本问题的答案。根据最近的因果关系心理学理论，人们的因果判断是基于评估一个假定的原因是否（以及如何）对结果产生影响。至关重要的是，这些判断取决于反事实的对比，即如果假定的原因没有发生，会发生什么？这包括在心理模拟中设想现实世界的替代可能性。为此，视觉化有很大的潜力来支持人们的反事实思维，特别是在面对复杂的场景时。然而，到目前为止，很少有工作已经解决了反事实可视化的背景下，帮助因果decisionmaking.本研究将探讨新的因果反事实可视化，这将，在对比的直接可视化的真实的数据，有一个新的功能，使因果反事实，并没有发生在现实中。反事实将由一个反事实模拟模型生成，该模型是用真实的数据训练的。这扩展了标准的数据可视化，通过可视化真实的数据之外的假设样本。它将通过使决策者能够交互式地创建合成数据并审查“封闭的可能世界”（例如，一个小的因果变化的不同结果），支持“举例说明”。可视化具体的范例将允许人们查看关键证据，并与反事实进行辩论，以获得可操作的见解。因果反事实可视化将得到该团队成员在心理学和人工智能领域的最新进展的支持。通过汇集来自多学科团队的专业知识，新的因果反事实可视化技术将提供一个有用的渠道，以评估和促进我们对人类行为和因果决策表现的理解，特别是关于形象化的作用。我们将在临床案例研究的背景下进行一系列心理学实验，以探讨医疗保健，这将涉及与医生的参与和共同设计。医生将使用可视化工具在涉及两个临床病例的心理学实验中做出和测试因果假设，以进行决策：（1）基于肺癌发生的因果风险因素的临床判断，目标是个体患者的单位水平因果关系;（2）通过测量药物对糖尿病治疗的因果作用进行临床试验，目标是人群水平的因果关系。通过定制的模拟，可视化将揭示替代治疗的潜在结果及其在疗效和副作用方面的权衡，以便通过可视化具体的反事实（例如医学测试，筛查，图像）进行比较。此外，研究人员可以在模拟临床试验中评估治疗组和对照组之间的差异，并查看违反他们假设的例子。通过这两个临床用例，研究成果将在医疗环境中直接测量，临床医生面临着复杂的医学环境，来自多个来源的大量输入和相互作用的数据将做出重要的临床决策。这项工作将展示新的可视化如何寻求解决这一问题，从而减少变异性，并通过临床医生可以解释的可操作见解支持稳健的决策。