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Multi-treatment Effect Estimation from Biomedical Data

arXiv 2112.07574 ↗First posted 14 Dec 2021cs.LG stat.MLcited by 0

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Abstract (arXiv)

This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, continuous and binary treatments, and many covariates. We compared M3E2 with three baselines in three synthetic benchmark datasets: two with multiple treatments and one with one treatment. Our analysis showed that our method has superior performance, making more assertive estimations of the multiple treatment effects.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 14 Dec 2021.

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References · 18

  1. 2017Attention Is All You Needcited by 59,932
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  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
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  7. 2018The Blessings of Multiple Causescited by 103
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  10. 2021A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Modelscited by 29
  11. 2019On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternativescited by 20
  12. 2021Heterogeneous Multi-task Learning with Expert Diversitycited by 18
6 more
  1. 2020Identifying effects of multiple treatments in the presence of unmeasured confoundingcited by 16
  2. 2018Multiple Causal Inference with Latent Confoundingcited by 14
  3. 2020Regret Minimization for Causal Inference on Large Treatment Spacecited by 7
  4. 2021Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confoundingcited by 7
  5. 2020ParKCa: Causal Inference with Partially Known Causescited by 4
  6. 2020Hi-CI: Deep Causal Inference in High Dimensionscited by 4

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