Deep Counterfactual Networks with Propensity-Dropout
This addresses causal inference for researchers and practitioners dealing with observational data, representing an incremental improvement over existing methods.
The paper tackles the problem of inferring individualized causal effects from observational data by proposing a deep multitask network with propensity-dropout regularization, and experiments show it outperforms state-of-the-art methods.
We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art.