+ The problem studied in this paper is definitely important in many real-world applications, such as robotics decision-making and autonomous driving. Discovering the underlying causation is important for agents to make reasonable decisions, especially in dynamic environments.
+ The method proposed in this paper is interesting and technically correct. Intuitively, using GRU to extract sequential information helps capture the changes of causal graphs.
- The main idea of causal discovery by sampling intervention set and causal graphs for masking is similar to DCDI [1]. This paper is more like using DCDI in dynamic environments, which may limit the novelty of this paper.
- The paper is not difficult to follow, but there are several places that are may cause confusion. (listed in point 3).
- The contribution of this paper is not fully supported by experiments.
Main Questions
(1) During the inference stage, why use samples instead of directly taking the argmax of Bernoulli distribution? How many samples are required? Will this sampling cause scalability problems?
(2) In the experiment part, the authors only compare with one method (V-CDN). Is it possible to compare DYNOTEARS with the proposed method?
(3) The authors mention that there is no ground truth to evaluate the causal discovery task. I agree with this opinion since the real world does not provide us causal graphs. However, the first experiment is conducted on a synthetic dataset, where I believe it is able to obtain the causation by checking collision conditions. In other words, I am not convinced only by the prediction results. Could the author provide the learned causal graphs and intervention sets and compare them with ground truth even on a simple synthetic dataset?
Clarification questions
(1) It seems the citation of NOTEARS [2] is wrongly used for DYNOTEARS [3]. This citation is important since DYNOTEARS is one of the motivations of this paper.
(2) ICM part in Figure 3 is not clear. How is the Intervention set I is used? If I understand correctly, function f
is a prediction model conditioned on history frames.
(3) The term “Bern” in equation (3) is not defined. I assume it is the Bernoulli distribution. Then what does the symbol B e r n ( α t , β t ) mean?
(4) According to equation (7), each node j
has its own parameters ϕ j t and ψ j t
. Could the authors explain why the parameters are related to time?
(5) In equation (16), the authors mention the term “ secondary optimization”. I can’t find any reference for it. Could the author provide more information?
Minor things:
(1) In the caption of Figure 2, the authors say “For nonstationary causal models, (c)….”. But in figure (c) belongs to stationary methods.
[1] Brouillard P, Lachapelle S, Lacoste A, et al. Differentiable causal discovery from interventional data[J]. arXiv preprint arXiv:2007.01754, 2020.
[2] Zheng X, Aragam B, Ravikumar P, et al. Dags with no tears: Continuous optimization for structure learning[J]. arXiv preprint arXiv:1803.01422, 2018.
[3] Pamfil R, Sriwattanaworachai N, Desai S, et al. Dynotears: Structure learning from time-series data[C]//International Conference on Artificial Intelligence and Statistics. PMLR, 2020: 1595-1605.
- The paper is not difficult to follow, but there are several places that are may cause confusion. (listed in point 3).