Causality is a bit harder to integrate with current machine learning models as it&#x27;s hard even with standard probabilistic graphical models. On the other hand, there has been a lot of work integrating deep neural networks with probabilistic models.<p>For example, the variational auto-encoders are a graphical model with Gaussian latent variables whose mean and variance are determined by (deep) neural networks [1]. There has been work exploring the neural network weights as latent variables themselves [2]. Finally, some new developments such as dropout can be interpreted as some form of deep Gaussian processes [3].<p>I believe there will be a lot further developments on this area in the near-future.<p>[1] <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1312.6114" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1312.6114</a><p>[2] <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1505.05424" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1505.05424</a><p>[3] <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1506.02142" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1506.02142</a>