Corpus ID: 61153451

Classifying Treatment Responders Under Causal Effect Monotonicity

@inproceedings{Kallus2019ClassifyingTR,
  title={Classifying Treatment Responders Under Causal Effect Monotonicity},
  author={Nathan Kallus},
  booktitle={ICML},
  year={2019}
}
  • Nathan Kallus
  • Published in ICML 2019
  • Mathematics, Computer Science
  • In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples of features, treatment indicator (e.g., drug/no drug), and a binary outcome (e.g., recovery from disease). As a classification task, the problem is made difficult by not knowing the example outcomes under the opposite treatment indicators. We assume the effect is monotonic, as in advertising's effect on a purchase or… CONTINUE READING

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