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Hypothesis-Based Weight of Evidence: An Approach to Assessing Causation and its Application to Regulatory Toxicology

机译：基于假设的证据权重：一种评估因果关系的方法及其在监管毒理学中的应用

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Other papers in this symposium focus on combining direct observations or measurements of a phenomenon of interest. Here, I consider the distinct problem of integrating diverse kinds of data to address the scientific case for toxicological causation in view of information that usually contains gaps and outright contradictions. Existing weight-of-evidence approaches have been criticized as either too formulaic or too vague, simply calling for professional judgment that is hard to trace to its scientific basis. I discuss an approachhypothesis-based weight of evidencethat emphasizes articulation of the hypothesized generalizations, their basis, and span of applicability. Hypothesized common processes should be expected to act elsewhere as wellin different species or different tissuesand so outcomes that ought to be affected become part of the evidence evaluation. A compelling hypothesis is one that provides a common unified explanation for observed results. Any apparent exceptions and failures to account for some data must be plausibly explained. Ad hoc additions to the explanations introduced to save hypotheses from apparent contradiction weaken the degree to which available data test causal propositions. In the end, we need an account of all the results at hand, specifying what is ascribed to hypothesized common causal processes and what to special exceptions, chance, or other factors. Evidence is weighed by considering comparative plausibility of an account including the proposed causal effect versus an alternative that explains all of the results at hand otherwise.
机译：本次研讨会上的其他论文集中于对感兴趣现象的直接观察或测量相结合。在这里，我考虑到通常包含空白和完全矛盾的信息，因此整合各种数据来解决毒理学成因的科学案例是一个独特的问题。现有的证据权重方法已经被批评为过于公式化或过于含糊，仅要求做出难以追溯其科学依据的专业判断。我讨论了一种基于方法假设的证据权重，强调了对假设的概括，其基础和适用范围的阐述。虚拟化的共同过程应预期会在其他物种或不同组织中发挥作用，因此应受到影响的结果将成为证据评估的一部分。令人信服的假设是为观察到的结果提供通用的统一解释的假设。必须合理地解释任何明显的例外和无法解释某些数据的问题。为使假设免受明显矛盾而引入的解释的临时补充，削弱了可用数据检验因果关系的程度。最后，我们需要对所有现有结果进行说明，详细说明归因于假设的常见因果过程以及特殊例外，机会或其他因素。通过考虑一个包括拟议的因果效应的账户与一个可以解释所有现有结果的备选方案的比较合理性来权衡证据。

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