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A directed acyclic graph for interactions.

Anton NilssonCarl BonanderUlf StrömbergJonas Björk
Published in: International journal of epidemiology (2021)
The IDAG allows for a both intuitive and stringent way of illustrating interactions. It helps to distinguish between causal and non-causal mechanisms behind effect variation. Conclusions about how to empirically estimate interactions can be drawn-as well as conclusions about how to achieve generalizability in contexts where interest lies in estimating an overall effect.
Keyphrases
  • convolutional neural network
  • deep learning