Targeted-BEHRT: deep learning for observational causal inference on longitudinal electronic health records
Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of “doubly robust” nonparametric tools coupl...
| Hlavní autoři: | , , , , , , , |
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| Médium: | Journal article |
| Jazyk: | English |
| Vydáno: |
IEEE
2022
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