Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

CAN SCIENCE AND RELIGION RESPOND TO CLIMATE CHANGE?

With the challenge of communicating climate science in the United States and making progress in international negotiations on climate change there is a need for other approaches. The moral issues of ecological degradation and climate justice need to be integrated into social consciousness, political legislation, and climate treaties. Both science and religion can contribute to this integration with differentiated language but shared purpose. Recognizing the limits of both science and religion is critical to finding a way forward for addressing the critical challenges of climate change. How we value nature and human–Earth relations is crucial to this. We need a broader environmental ethics in dialogue with the science of climate change.     

Reducing the risk of bias in health behaviour change trials: Improving trial design,reporting or bias assessment criteria? A review and case study

Objective: It has been suggested that randomised controlled trials (RCTs) of health behaviour change (HBC) interventions are less rigorously designed than – for example– drug trials. This study presents an approach to clarifying whether this is due to poor trial design, incomplete trial reporting and/or the inappropriateness of commonly applied risk of bias assessment criteria.

Design: First, a framework of key sources of bias and common strategies for reducing bias risk is developed based on a literature review. Second, we describe the design of a multi-site RCT evaluating the cost-effectiveness of an HIV-treatment adherence intervention (case study). The choices made by the multidisciplinary team trying to minimise the risk of bias are compared against the risk of bias framework.

Main outcome measures: Implementation of common strategies for reducing the risk of bias in the case study; alternative or additional strategies applied; a justification for each deviation from the risk of bias framework.

Results: Most of the common strategies for reducing the risk of bias could be implemented. Alternative strategies were developed for minimising the risk of performance bias and contamination. Several additional, domain-specific risk of bias strategies were implemented.

Conclusions: The literature provides useful guidance for reducing the risk of bias in HBC trials. Yet, the case study suggests that HBC trial designers may face specific challenges that require alternative/additional measures for reducing the risk of bias. Using the risk of bias justification table (RATIONALE) could lead to better-designed HBC trials, more comprehensive trial reports and the data necessary for evaluating the appropriateness of commonly applied risk of bias assessment criteria to HBC trials.