Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: Summary:
   The authors consider the problem of estimating average treatment effects when observed X and treatment T causes Y. Observational data for X,T,Y is available and strong ignorability is assumed. Previous work (Shalit et al 2017) introduced learning a representation that is invariant in distribution across treatment and control groups and using that with treatment to estimate Y. However, authors point out that this representation being forced to be invariant still does not drive the selection bias to zero. A follow up work (Hassanpour and Greiner 2019) - corrects for this by using additional importance weighting that estimates the treatment selection bias given the learnt representation. However, the authors point out even this is not complete in general, as X could be determined by three latent factors, one that is the actual confounder between treatment and outcome and the other that affects only the outcome and the other that affects only the treatment. Therefore, the authors propose to have three representations and enforce independence between representation that solely determines outcome and the treatment and make other appropriate terms depend on the respective latent factors. This gives a modified objective with respect to these two prior works.

The authors implement optimize this joint system on synthetic and real world datasets. They show that they outperform all these previous works because of explicitly accounting for confounder, latent factors that solely control only outcome and treatment assignment respectively. 

Pros:
  This paper directly addresses the problems due to Shalit 2017 that are still left open. The experimental results seems convincing on standard benchmarks. 

I vote for accepting the paper. I don't have many concerns about this paper.

Cons:
  - I have one question for the authors - if T and Y(0),Y(1) are independent given X is assumed, then how are we sure that the composite representations (of the three latent factors) are going to necessarily satisfy ignorability provably ?? I guess this cannot be formally established. It would be great for the authors to comment on this.



review_title: Official Blind Review #1

final_score: rating: 8: Accept
review_content: The paper proposes a new way of estimating treatment effects from observational data, that decouples (disentangles) the observed covariates X into three sets: covariates that contributed to the selection of the treatment T, covariates that cause the outcome Y and covariates that do both. The authors show that by leveraging this additional structure they can improve upon existing methods in both ITE and ATE 

The main contributions of the paper are:
* Highlighting the importance of differentiating between treatment and outcome inducing factors and proposing an algorithm to detect the two
* Creating a joint optimisation model that contains the factual loss, the cross entropy (treatment) loss and the imbalance loss

Overall, I like the paper quite a lot, I find it well-written and clearly motivated with a very nice experimental section that it is designed around understanding the behaviour of the proposed model.

In terms of suggestions, I think it will be very interesting to link the approaches using invariant causal representations with existing work in the Counterfactual Risk Minimization [1] literature and to mutualise the experimental setup. 

[1] Swaminathan, Adith, and Thorsten Joachims. "Counterfactual risk minimization: Learning from logged bandit feedback." International Conference on Machine Learning. 2015.
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: 
The paper proposes an algorithm that identifies disentangled representation to find out an individual treatment effect. A very specific model that tries to find out the underlying dynamics of such a problem is proposed and is learned by minimizing a suggested objective that takes the strengths of previous approaches. The method is demonstrated in a synthetic dataset and IHDP dataset and shown to outperform other previous methods by a large margin.

My initial review was negative, but I changed my mind after reading a few papers in this area. It seems that explicit learning of underlying factors that are described in (Hassanpour & Greiner, 2019) is a nice idea and works well. My only concern is that the paper has a lot of overlap with (Hassanpour & Greiner, 2019), even using identical figures. I am not sure whether it is OK.


review_title: Official Blind Review #3

Meta-review: The paper proposes a new way of estimating treatment effects from observational data. The text is clear and experiments support the proposed model.
Decision: Accept (Poster)