因果过程追踪方法在公共政策评估中的应用及展望

Application and Prospect of Causal Process Tracing(CPT)in Public Policy Evaluation

量化方法和实验方法作为主流政策评估方法,常常面临难以处理变量内生性的困境。因果过程追踪方法在应用于公共政策评估时,并不预先筛选影响政策结果的变量,而是追踪探寻变量链式联动变化的因果机制,消解只评估少数变量的内生性难题,形成评估复杂政策效应的独特优势。因果过程追踪方法首先将政策因果机制转化为理论假设,之后将变量串联为推动政策发展的因果链条,然后收集不同类型证据,将实际发现的证据和理论预测的证据进行一致性比对,并衡量证据权重,最后评估并检验因果机制假设的效度。因果过程追踪方法与贝叶斯概率推断、实验方法等的结合应用,进一步提高了政策评估的精准程度。将因果过程追踪方法应用于中国公共政策评估,一方面有利于从政策特殊性中抓取一般性,另一方面有助于解释政策目标的“偏移”,推动政策目标的实现。

As the mainstream policy evaluation methods,quantitative methods and experimental methods often face the dilemma that it is difficult to deal with the endogenous variables.When applied to public policy evaluation,the Causal Process Tracing method(CPT)does not pre-screen the variables that affect the policy results.Instead,the method tracks and explores the causal mechanism of variables reacting in chain,where avoids risk that overshadows potential variables,confounding bias and spurious association.Firstly,we transform the causal mechanism into a theoretical hypothesis through the CPT.Secondly,we trace outcomes back to their sources,meaningfully learning about how causal mechanisms work.Thirdly,different types of evidences are collected by the approach.Fourthly,we compare the actually found evidences with the theoretically predicted evidences and measure the weights of the evidences.Finally,the causal mechanism hypothesis is tested.The CPT could be friendly combined with other evaluation methods,such as,Bayesian probability inference and experimental methods,which leads to stronger causal inferences as a result.The CPT is helpful to Chinese construction of public policy evaluation system.It could not only help grasp generality from the particularity of China's policies but also explain the deviation from policy objectives and properly attain the policy goal.