Generalized Adjustment Under Confounding and Selection Biases

@inproceedings{Correa2018GeneralizedAU,
  title={Generalized Adjustment Under Confounding and Selection Biases},
  author={Juan D. Correa and Jin Tian and Elias Bareinboim},
  booktitle={AAAI},
  year={2018}
}
Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment. We further design an algorithm for listing all… CONTINUE READING

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