[fpr 2157] Rubin





さた、最後のグループの紹介で少し話のでた、Rubin's causal modelとガイドライン


Statistical Methods in Psychology Journals: Guidelines and Explanations

Causality.  Inferring causality from nonrandomized designs is a risky
enterprise. Researchers using nonrandomized designs have an extra obligation
to explain the logic behind covariates included in their designs and to
alert the reader to plausible rival hypotheses that might explain their
results. Even in randomized experiments, attributing causal effects to any
one aspect of the treatment condition requires support from additional

It is sometimes thought that correlation does not prove causation but
"causal modeling" does. Despite the admonitions of experts in this field,
researchers sometimes use goodness-of-fit indices to hunt through thickets
of competing models and settle on a plausible substantive explanation only
in retrospect. McDonald (1997), in an analysis of a historical data set,
showed the dangers of this practiceand the importance of substantive theory.
Scheines, Spirites, Glymour, Meek, and Richardson (1998; discussions
following) offer similar cautions from a theoretical standpoint.

A generally accepted framework for formulating questions concerning the
estimation of causal effects in social and biomedical science involves the
use of "potential outcomes," with one outcome for each treatment condition.
Although the perspective has old roots, including use by Fisher and Neyman
in the context of completely randomized experiments analyzed by
randomization-based inference ( Rubin, 1990b), it is typically referred to
as "Rubin's causal model" or RCM ( Holland, 1986). For extensions to
observational studies and other forms of inference, see Rubin (1974, 1977,
1978). This approach is now relatively standard, even for settings with
instrumental variables and multistage models or simultaneous equations.


スレッド表示 著者別表示 日付順表示 トップページ