Towards Modular Learning of Deep Causal Generative Models

Shpitser & Pearl (2008) proposed sound and complete algorithms to compute identifiable observational, interventional, and counterfactual queries for certain causal graph structures. However, these algorithms assume that we can correctly estimate the joint distributions, which is impractical for high-dimensional datasets. During the current rise of foundational models, we have access to large pre-trained models to generate realistic high-dimensional samples. To address the causal inference problem with high dimensional data, we propose a sequential adversarial training algorithm for learning deep causal generative models by dividing the training problem into independent sub-parts, thereby enabling the use of such pre-trained models. Our proposed algorithm called WhatIfGAN, arranges generative models according to a causal graph and trains them to imitate the underlying causal model even with unobserved confounders. Finally, with a semi-synthetic Colored MNIST dataset, we show that WhatIfGAN can sample from identifiable causal queries involving high-dimensional variables.