Title: Implicit Causal Models for Genome-wide Association Studies,
Abstract: Progress in probabilistic generative models has accelerated, developing
richer models with neural architectures, implicit densities, and with scalable
algorithms for their Bayesian inference. However, there has been limited
progress in models that capture causal relationships, for example, how
individual genetic factors cause major human diseases. In this work, we focus
on two challenges in particular: How do we build richer causal models, which
can capture highly nonlinear relationships and interactions between multiple
causes? How do we adjust for latent confounders, which are variables
influencing both cause and effect and which prevent learning of causal
relationships? To address these challenges, we synthesize ideas from causality
and modern probabilistic modeling. For the first, we describe implicit causal
models, a class of causal models that leverages neural architectures with an
implicit density. For the second, we describe an implicit causal model that
adjusts for confounders by sharing strength across examples. In experiments, we
scale Bayesian inference on up to a billion genetic measurements. We achieve
state of the art accuracy for identifying causal factors: we significantly
outperform existing genetics methods by an absolute difference of 15-45.3%.