Causal Analyses in Social Sciences

休谟提出的因果问题影响了两个半世纪的科学哲学讨论,即人类如何从有限经验观察推论必然因果关系?密尔从方法论角度阐释了因果推论的逻辑,指出只有在其他因素相同的条件下,经验相关才能推论因果,为现代随机分组试验设计奠定了基础。实验方法在社会科学中应用有限,更为常用的是观察数据统计分析和个案数据定性分析。复杂统计模型用统计控制模拟实验控制,也能有效检验因果命题。定性分析是理论与经验的反复碰撞,有利于提出新理论或新假设。本文强调,因果解释一定包括机制解释,而机制解释一定是理论解释。因果理论不是对经验相关的简单归纳,而是思想飞跃。检验因果理论的基本逻辑是:从理论演绎因果命题,再演绎相关假设,然后用统计数据检验之。最后讨论整合理论、定性、定量的三点定位原则。

Hume’s problem of causal induction has inspired more than two centuries of epistemological discussion． J． S． Mill was the first one to elaborate the methodological principles of causal induction． The Millian principle of“overall similarities” for causal inference laid the foundation for Fisher’s randomized experimental design and guided causal analyses in multivariate statistical modeling and qualitative comparative case studies． Causal theories are not，however，induced from empirical correlations． They are produced through leaps of faith and empirically tested through deductive logic． The best research strategy is triangulation that combines theorizing， qualitative analysis，and statistical modeling．

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