Multiple Treatment Effect Estimation using Deep Generative Model with Task Embedding

Causal inference using observational data on multiple treatments is an important problem in a wide variety of fields. However, the existing literature tends to focus only on causal inference in case of binary or multinoulli treatments. These models are either incompatible with multiple treatments, or extending them to multiple treatments is computationally expensive. We use a previous formulation of causal inference using variational autoencoder (VAE) and propose a novel architecture to estimate the causal effect of any subset of the treatments. The higher order effects of multiple treatments are captured through a task embedding. The task embedding allows the model to scale to multiple treatments. The model is applied on real digital marketing dataset to evaluate the next best set of marketing actions. For evaluation, the model is compared against competitive baseline models on two semi-synthetic datasets created using the covariates from the real dataset. The performance is measured along four evaluation metrics considered in the causal inference literature and one proposed by us. The proposed evaluation metric measures the loss in the expected outcome when a particular model is used for decision making as compared to the ground truth. The proposed model outperforms the baselines along all five evaluation metrics. It outperforms the best baseline by over 30% along these evaluation metrics. The proposed approach is also shown to be robust when a subset of the confounders is not observed. The results on real data show the importance of the flexible modeling approach provided by the proposed model.