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Predicting the effect of unseen interventions is a fundamental research question across the data sciences.
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Steffen L Lauritzen and David J Spiegelhalter · 1988
Earlier work this paper cites.
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J. Pearl · 1988
Earlier work this paper cites.
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James M Robins · 1989
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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