Causal inference using deep neural networks

Ye Yuan; Xueying Ding; Ziv Bar-Joseph

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arXiv · 2011.12508

Causal inference using deep neural networks

Ye Yuan·Xueying Ding·Ziv Bar-Joseph

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Abstract

Causal inference from observation data is a core problem in many scientific fields. Here we present a general supervised deep learning framework that infers causal interactions by transforming the input vectors to an image-like representation for every pair of inputs. Given a training dataset we first construct a normalized empirical probability density distribution (NEPDF) matrix. We then train a convolutional neural network (CNN) on NEPDFs for causality predictions. We tested the method on several different simulated and real world data and compared it to prior methods for causal inference. As we show, the method is general, can efficiently handle very large datasets and improves upon prior methods.

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Ye Yuan, Xueying Ding, Ziv Bar-Joseph. 2020-11-25. Causal inference using deep neural networks. https://arxiv.org/abs/2011.12508

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