M3E2: Multi-gate Mixture-of-experts for Multi-treatment Effect Estimation
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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 is robust to 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 true treatment effects. The code is available at github.com/raquelaoki/M3E2.
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