Causal Explanatory Power
Causal Explanatory Power
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DOI：
10.1093/bjps/axy012
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发表时间：
2019-12-01
影响因子：
3.4
通讯作者：
Stern, Reuben
中科院分区：
文献类型：
--
作者：
Eva, Benjamin;Stern, Reuben
Schupbach和Sprenger([2011])引入了一种新的概率方法来衡量一个给定的解释对相应的解释施加的解释力。尽管我们同情他们的一般方法，但我们认为(未经修改)它没有充分地捕捉到c对e施加的因果解释能力随背景知识而变化的方式。然后，我们修改他们的方法，使其能够捕捉到这种差异。虽然我们对解释能力的描述没有舒普巴赫和斯普林格的解释能力那么雄心勃勃，因为它仅限于因果解释能力，但它也更雄心勃勃，因为我们没有将其范围限制在c真正解释e的情况下。相反，我们声称c因果解释e的充要条件是我们的描述表明c以某种正量的因果解释能力解释e。
Schupbach and Sprenger ([2011]) introduce a novel probabilistic approach to measuring the explanatory power that a given explanans exerts over a corresponding explanandum. Though we are sympathetic to their general approach, we argue that it does not (without revision) adequately capture the way in which the causal explanatory power that c exerts on e varies with background knowledge. We then amend their approach so that it does capture this variance. Though our account of explanatory power is less ambitious than Schupbach and Sprenger's in the sense that it is limited to causal explanatory power, it is also more ambitious because we do not limit its domain to cases where c genuinely explains e. Instead, we claim that c causally explains e if and only if our account says that c explains e with some positive amount of causal explanatory power.