48
个编辑
更改
→因果模型 Causal models
====因果模型 Causal models====
====因果模型 Causal models====
The ''causal models framework'' analyzes counterfactuals in terms of systems of [[structural equation model|structural equations]]. In a system of equations, each variable is assigned a value that is an explicit function of other variables in the system. Given such a model, the sentence "''Y'' would be ''y'' had ''X'' been ''x''" (formally, ''X = x'' > ''Y = y'' ) is defined as the assertion: If we replace the equation currently determining ''X'' with a constant ''X = x'', and solve the set of equations for variable ''Y'', the solution obtained will be ''Y = y''. This definition has been shown to be compatible with the axioms of possible world semantics and forms the basis for causal inference in the natural and social sciences, since each structural equation in those domains corresponds to a familiar causal mechanism that can be meaningfully reasoned about by investigators. This approach was developed by [[Judea Pearl]] (2000) as a means of encoding fine-grained intuitions about causal relations which are difficult to capture in other proposed systems.<ref name="Pearl2000">{{Cite book |last=Pearl |first=Judea |title=Causality |publisher=Cambridge University Press |year=2000 }}</ref>
The '''<font color="#ff8000">因果模型框架 Causal Models Framework</font>''' analyzes counterfactuals in terms of systems of '''<font color="#ff8000">结构方程模型 Structural Equation Model</font>'''[[structural equations]]. In a system of equations, each variable is assigned a value that is an explicit function of other variables in the system. Given such a model, the sentence "''Y'' would be ''y'' had ''X'' been ''x''" (formally, ''X = x'' > ''Y = y'' ) is defined as the assertion: If we replace the equation currently determining ''X'' with a constant ''X = x'', and solve the set of equations for variable ''Y'', the solution obtained will be ''Y = y''. This definition has been shown to be compatible with the axioms of possible world semantics and forms the basis for causal inference in the natural and social sciences, since each structural equation in those domains corresponds to a familiar causal mechanism that can be meaningfully reasoned about by investigators. This approach was developed by [[Judea Pearl]] (2000) as a means of encoding fine-grained intuitions about causal relations which are difficult to capture in other proposed systems.<ref name="Pearl2000">{{Cite book |last=Pearl |first=Judea |title=Causality |publisher=Cambridge University Press |year=2000 }}</ref>
''因果模型框架''从结构方程（structural equations）系统的角度分析反事实。在一个方程系统中，每个变量都被分配了一个值，这个值是系统中其他变量的显式函数。给定这样一个模型，“如果X是X，Y就会是Y（''Y'' would be ''y'' had ''X'' been ''x''）”这个句子 (形式上为 ''X = x'' > ''Y = y'' )被定义为断言。如果我们用一个常数''X = x''取代当前决定 ''X''的方程，并求解变量''Y''的方程组，得到的解将是''Y = y''。这个定义已被证明与可能世界语义学的公理兼容，并构成自然科学和社会科学中因果推理的基础。因为这些领域的每个结构方程都对应于一个熟悉的因果机制，这个因果机制可以被研究者进行有意义地推理。这种方法是由Judea Pearl(2000)提出的，作为编码关于因果关系的细粒度直觉的手段，这些直觉在其他提议的系统中难以捕捉。<ref name="Pearl2000">{{Cite book |last=Pearl |first=Judea |title=Causality |publisher=Cambridge University Press |year=2000 }}</ref>
''因果模型框架''从结构方程（structural equations）系统的角度分析反事实。在一个方程系统中，每个变量都被分配了一个值，这个值是系统中其他变量的显式函数。给定这样一个模型，“如果X是X，Y就会是Y（''Y'' would be ''y'' had ''X'' been ''x''）”这个句子 (形式上为 ''X = x'' > ''Y = y'' )被定义为断言。如果我们用一个常数''X = x''取代当前决定 ''X''的方程，并求解变量''Y''的方程组，得到的解将是''Y = y''。这个定义已被证明与可能世界语义学的公理兼容，并构成自然科学和社会科学中因果推理的基础。因为这些领域的每个结构方程都对应于一个熟悉的因果机制，这个因果机制可以被研究者进行有意义地推理。这种方法是由Judea Pearl(2000)提出的，作为编码关于因果关系的细粒度直觉的手段，这些直觉在其他提议的系统中难以捕捉。<ref name="Pearl2000">{{Cite book |last=Pearl |first=Judea |title=Causality |publisher=Cambridge University Press |year=2000 }}</ref>