Statistics and causality: separated to reunite-commentary on Bryan Dowd's "separated at birth".
Statistics and causality: separated to reunite-commentary on Bryan Dowd's "separated at birth".
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统计与因果关系：分离到重聚——评布莱恩·多德的《出生时分离》。
DOI：
10.1111/j.1475-6773.2011.01243.x
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发表时间：
2011
影响因子：
3.4
通讯作者：
Pearl,Judea
中科院分区：
文献类型：
--
作者：
Pearl,Judea
值得称赞的是，布莱恩·多德(2010)为我们揭示了统计学家和计量经济学家之间紧张关系的历史根源，直到今天，这种紧张关系仍然延续着因果推理令人困惑、神秘或有争议的神话。虽然现代分析已经证明这一神话是没有根据的，但往往是历史记载将事情放在了正确的角度。我认为统计学和经济学之间的紧张，或者更广泛地说，统计学和因果关系之间的紧张，根源于一种比多德描述的更根本的分裂。此外，与多德的叙述相反，我认为分裂是合理的、必要的，而且没有得到足够的重视。事实上，只有在统计学和因果概念之间的区别通过新的数学符号变得清晰和正式之后，才出现了富有成效的共生，这两种范式现在都受益。多德的描述将这种分裂描述为不幸情况的产物，如果参与者更多地意识到彼此的工作，这种分裂是可以避免的。我们被告知，经济学家开发了因果推理技术，产生了对因果效应的回归估计(例如，混淆控制)，而且，由于回归是统计学的一项自豪发明，统计学家没有理由像他们那样强烈回避因果分析。如果他们真的反对结构方程、工具变量和观测研究，那一定是由于一种不幸的修辞区分，或者，也许是科学史上的一次偶然事件。
Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic, or controversial. While modern analysis has proven this myth baseless, it is often the historical accounts that put things in the proper perspective. I see the tension between statistics and economics or, more generally, between statistics and causality, to be rooted in a more fundamental schism than the one portrayed in Dowd’s account. Moreover, and contrary to Dowd’s narrative, I believe that the schism was justified, necessary, and not sufficiently emphasized. In fact, it was only after the distinction between statistical and causal concepts was made crisp and formal through new mathematical notation that a productive symbiosis has emerged which now benefits both paradigms.Dowd’s account portrays the schism as a product of unfortunate circumstances that could have been avoided, if only the players were more aware of each other work. Economists, we are told, developed causal inference techniques that yield regressional estimates of causal effects (eg, IV, confounding-control) and, since regression is a proud invention of statistics, there was no reason for statisticians to shun causal analysis as strongly as they did. If they did oppose structural equations, instrumental variables, and observational studies, it must have been due to an unfortunate rhetorical distinction or, perhaps, a fluke in the history of science.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
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影响因子：
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影响因子：
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影响因子：
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作者：
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通讯作者：
van der Laan, MJ