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

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.