Causal Inference in a Bayesian Network with unobservable backdoor and no frontdoor 
Just like the Bayesian network shown above. I want to identify the average treatment effect(ATE) from Smoking to Lung Cancer. If Genetics is observable, I can easily identify the ATE with Pearl's backdoor adjustment.
But now, the Genetics is unobservable. As shown in the network, there's no front-door path between Smoking to Lung Cancer. So how to identify the causal effect?

*

*All nodes can be observed except the Genetics.

*There're more nodes, but those nodes aren't in any paths between Smoking and Lung_Cancer. The whole network is shown below.



I'm sorry to say this but this seems to be a case of non-identifiability. You can't identify the ATE between Smoking and Lung Cancer there. There is an unmeasured confounder for Smoking and Lung Cancer, which is Genetics, and that's it. You can't adjust for it. There is indeed no node in the network between Smoking and Lung Cancer, which could help you with the front-door criterion, or between Genetics and Smoking or Genetics and Lung Cancer, which could help you with the back-door criterion.
You could adjust by a descendent of the unmeasured confounder (Attention Disorder) to "partially" adjust for it, but you wouldn't remove all the bias. For a crash course on good and bad controls, I recommend you A Crash Course in Good and Bad Controls by Carlos Cinelli.
