摘要
尽管演绎逻辑在推动分析哲学发展中起到了极其重要的作用,但是由于因果推理具有不同于演绎推理的特殊性,借助演绎法去刻画因果逻辑将面临若干困难。例如,保真与保义、有效性与信息性难以兼顾的二难;外延适当性与内涵适当性、扩大性与非扩大性、单调性与非单调性之间的对立。这就是因果推理面临的“演绎之困”。面临上述困局,因果模态方法和反事实方法有所作为但无力回天。人工智能科学家提出的因果结构模型理论兼顾了确定性与不确定性两方面,尝试超越上述截然二分和根本对立,在较大程度上破解了难题和困局。
In this paper,we mainly discuss the logical and philosophical problems faced by the study of causality.We try to reveal the“The Trouble with Deduction”in causal inference.That is,we try to solve the problem between truth-preservation and meaning-preservation,and the difficulty between validity and information in causal inference.Then,we discuss how to keep balance between extension and intention appropriateness,expansion and non-expansion,and,monotonicity and non-monotonicity.After that,we reveal the limitations of modal approaches and counterfactual to causality.Finally,we show the advantages of causal structural model,and look forward to the further development of causal logic.
出处
《哲学分析》
CSSCI
2022年第4期137-151,199,共16页
Philosophical Analysis
关键词
因果逻辑
因果推理
反事实方法
因果结构模型
causal logic
causal inference
counterfactual approaches
causal structural model
