Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if‐then relations, and evidence. Decision‐related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health‐related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM‐like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.     

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Some Limitations of “Risk = Threat × Vulnerability × Consequence” for Risk Analysis of Terrorist Attacks

Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk=Threat×Vulnerability×Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP?) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk‐scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two‐level (or few‐level) hierarchical optimization models can provide a useful alternative to Risk=Threat×Vulnerability×Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two‐level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.     

What's Wrong with Risk Matrices?

Risk matrices—tables mapping "frequency" and "severity" ratings to corresponding risk priority levels—are popular in applications as diverse as terrorism risk analysis, highway construction project management, office building risk analysis, climate change risk management, and enterprise risk management (ERM). National and international standards (e.g., Military Standard 882C and AS/NZS 4360:1999) have stimulated adoption of risk matrices by many organizations and risk consultants. However, little research rigorously validates their performance in actually improving risk management decisions. This article examines some mathematical properties of risk matrices and shows that they have the following limitations. (a) Poor Resolution . Typical risk matrices can correctly and unambiguously compare only a small fraction (e.g., less than 10%) of randomly selected pairs of hazards. They can assign identical ratings to quantitatively very different risks ("range compression"). (b) Errors . Risk matrices can mistakenly assign higher qualitative ratings to quantitatively smaller risks. For risks with negatively correlated frequencies and severities, they can be "worse than useless," leading to worse-than-random decisions. (c) Suboptimal Resource Allocation . Effective allocation of resources to risk-reducing countermeasures cannot be based on the categories provided by risk matrices. (d) Ambiguous Inputs and Outputs . Categorizations of severity cannot be made objectively for uncertain consequences. Inputs to risk matrices (e.g., frequency and severity categorizations) and resulting outputs (i.e., risk ratings) require subjective interpretation, and different users may obtain opposite ratings of the same quantitative risks. These limitations suggest that risk matrices should be used with caution, and only with careful explanations of embedded judgments.     

Children's Attachment Relationships with Day Care Caregivers: Associations with Positive Caregiving and the Child's Temperament

In this study, children's attachment relationships with their professional caregivers in center day care were observed for 48 children. We explored whether more positive caregiving was associated with a more secure attachment relationship and whether this association was stronger for more temperamentally irritable children compared to less irritable children. Trained observers coded the attachment relationship in the day care setting using the attachment Q-sort. The observational record of the caregiving environment was used to assess children's individual experience of positive caregiver–child interaction in the classroom. When caregivers showed more frequent positive caregiving behavior, children showed more secure attachment behavior toward their primary professional caregiver. Temperament was not related to attachment security, nor did it serve as a moderator. Consequently, no support for Belsky's susceptibility hypothesis was found.