Deep IV: A Flexible Approach for Counterfactual Prediction


Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1414-1423, 2017.


Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables. This paper provides a recipe for augmenting deep learning methods to accurately characterize such relationships in the presence of instrument variables (IVs) – sources of treatment randomization that are conditionally independent from the outcomes. Our IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework allows us to take advantage of off-the-shelf supervised learning techniques to estimate causal effects by adapting the loss function. Experiments show that it outperforms existing machine learning approaches.

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