Paper:
GANITE: ESTIMATION OF INDIVIDUALIZED TREAT- MENT EFFECTS USING GENERATIVE ADVERSARIAL NETS
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods.

Review:
This paper presents GANITE, a Generative Adversarial Network (GAN) approach for estimating Individualized Treatment Effects (ITE). This is achieved by utilising a GAN to impute the `missing` counterfactuals, i.e. the  outcomes of the treatments that were not observed in the training (i.e. factual) sample, and then using another GAN to estimate the ITE based on this `complete` dataset. The authors then proceed in combining the two GAN objectives with extra supervised losses to better account for the observed data; the GAN loss for the `G` network has an extra term for the `G` network to better predict the factual outcome `y_f` (which should be easy to do given the fact that y_f is an input to the network) and the GAN loss for the `I` network has an extra term w.r.t. the corresponding performance metric used for evaluation, i.e. PEHE for binary treatment and MSE for multiple treatments. This model is then evaluated on extensive experiments.
The paper is reasonably well-written with clear background and diagrams for the overall architecture. The idea is novel and seems to be relatively effective in practice although I do believe that it has a lot of moving parts and introduces a considerable amount of hyperameters (which generally are problematic to tune in causal inference tasks). Other than that, I have the following questions and remarks:
- I might have misunderstood the motivation but the GAN objective for the `G` network is a bit weird; why is it a good idea to push the counterfactual outcomes close to the factual outcomes (which is what the GAN objective is aiming for)? Intuitively, I would expect that different treatments should have different outcomes and the distribution of the factual and counterfactual `y` should differ.
- According to which metric did you perform hyper-parameter optimization on all of the experiments? 
- From the first toy experiment that highlights the importance of each of the losses it seems that the addition of the supervised loss greatly boosts the performance, compared to just using the GAN objectives. What was the relative weighting on those losses in general? 
- From what I understand the `I` network is necessary for out-of-sample predictions where you don’t have the treatment assignment, but for within sample prediction you can also use the `G` network. What is the performance gap between the `I` and `G` networks on the within-sample set? Furthermore, have you experimented with constructing `G` in a way that can represent `I` by just zeroing the contribution of `y_f` and `t`? In this way you can tie the parameters and avoid the two-step process (since `G` and `I` represent similar things).
- For figure 2 what was the hyper parameters for CFR? CFR includes a specific knob to account for the larger mismatches between treated and control distributions. Did you do hyper-parameter tuning for all of the methods in this task?
- I would also suggest to not use “between” when referring to the KL-divergence as it is not a symmetric quantity.
Also it should be pointed out that for IHDP the standard evaluation protocol is 1000 replications (rather than 100) so there might be some discrepancy on the scores due to that.