Causal inference by using invariant prediction: identification and confidence intervals
نویسندگان
چکیده
منابع مشابه
Causal inference using invariant prediction: identification and confidence intervals
What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on v...
متن کاملComments on “Causal inference using invariant prediction: identification and confidence intervals” by Peters, Bühlmann and Meinshausen
I consider that the genuine fundamental problem of causal inference is the need for (partially untestable) invariance assumptions to operationalize interventions, and I thank the authors for emphasizing the role of invariances in a stimulating paper. I would like to make some brief comments on how the ideas introduced here can also be helpful in the context of measurement problems. Much of the ...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2016
ISSN: 1369-7412
DOI: 10.1111/rssb.12167