Hydranet: A Neural Network for the Estimation of Multi-valued Treatment Effects

Neural network-based treatment effect estimation algorithms are well-known in the causal inference community. Many works propose designs and architectures and report performance metrics over benchmarking data sets, in a machine learning manner . Nevertheless, most authors focus solely on binary treatment scenarios. This is a limitation, as many real-world scenarios have a multivalued treatment nature (for instance, multiarmed clinical trials, or health technology assessment processes). In this work, a novel approach is presented, where a top-performing, neural network-based algorithm for binary treatment effect estimation is generalized to a multivalued treatment setting. This approach yields an estimator with desirable asymptotic properties that delivers very good results in a wide range of experiments. To the best of the authors’ knowledge, this work is opening ground for the benchmarking of neural network-based algorithms for multivalued treatment effect estimation.