Counterfactual domain adversarial training of neural networks
A number of domain adaptation techniques have been developed in classical supervised machine learning to solve the problem of learning under domain shift. Unfortunately, almost no attempt has been made to exploit their potential use for causal inference. In this paper, we present a conceptual framework which leverages on one of these successes by adopting Domain Adversarial of Neural Network (DANN) [1] and make it work within the context of causal inference. More specifically, we attempt to modify an existing counterfactual prediction framework based on domain adaptation (DA) [2] with a deep neural network and discrepancy distance measure to enable us instead to perform causal inference in an adversarial way. We achieved this by jointly minimizing the risk in the feature reconstruction and the counterfactual prediction while maximizing the error in discriminating between the distribution of subjects that received intervention and those that received no intervention. This way, a domain invariant yet discriminative model that will generalize well on the counterfactual target distribution is produced.
2017 International Conference on the Frontiers and Advances in Data Science (FADS)