Estimating counterfactual treatment outcomes over time through adversarially balanced representations
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.
The paper adapts domain adversarial training to construct treatment invariant representation for adjusting time-varying confounding in counterfactual treatment outcome predictions over time. The proposed method extends the recent static settings of balancing representation in counterfactual inference to longitudinal settings, and it also overcomes the problem of high-variance of IPTW weighting in MSN based methods such as the current state-of-art method RMSN. The paper is very well written. Method is novel to me. Experiments are sufficient. However since I am not very familiar with this area, so there can be things I miss in the evaluation of this paper. There is one problem with the illustrations in Figure 1: predictions on the potential outcomes before the first treatment was given should have stayed on the same path, and then depart when different treatment was initiated (at different time)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Estimating counterfactual treatment outcomes over time through adversarially balanced representations
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper adapts domain adversarial training to construct treatment invariant representation for adjusting time-varying confounding in counterfactual treatment outcome predictions over time. The proposed method extends the recent static settings of balancing representation in counterfactual inference to longitudinal settings, and it also overcomes the problem of high-variance of IPTW weighting in MSN based methods such as the current state-of-art method RMSN. The paper is very well written. Method is novel to me. Experiments are sufficient. However since I am not very familiar with this area, so there can be things I miss in the evaluation of this paper. There is one problem with the illustrations in Figure 1: predictions on the potential outcomes before the first treatment was given should have stayed on the same path, and then depart when different treatment was initiated (at different time)
assistant
The paper adapts domain adversarial training to construct treatment invariant representation for adjusting time-varying confounding in counterfactual treatment outcome predictions over time. The proposed method extends the recent static settings of balancing representation in counterfactual inference to longitudinal settings, and it also overcomes the problem of high-variance of IPTW weighting in MSN based methods such as the current state-of-art method RMSN. The paper is very well written. Method is novel to me. Experiments are sufficient. However since I am not very familiar with this area, so there can be things I miss in the evaluation of this paper. There is one problem with the illustrations in Figure 1: predictions on the potential outcomes before the first treatment was given should have stayed on the same path, and then depart when different treatment was initiated (at different time)