Paper:
In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients. For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes. This produces the so-called "weight transport problem" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli. This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm. However, such random weights do not appear to work well for large networks. Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem. The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design. We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights. As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST, SVHN, CIFAR-10 and VOC. Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.

Review:
The paper introduces a training mechanism for spiking neural nets that employs a causal inference technique, called RDD, for adjustment of backward spiking weights. This technique induces the backward influence strengths to be reciprocal to the forward ones, bringing desirable symmetry properties.
Pros:
 * The relationship between causal inference and biologically plausible learning is very interesting. This relationship is also important and impactful for the machine learning community, as we are on the quest of new deep learning technologies.
 * Application of the RDD method to spiking neural net training is novel. The reciprocal relationship of the causal effect to the synaptic strength is a very intuitive and elegant solution to the weight transport problem.
Cons:
 * From the reported results, it is not possible to decide whether RDD really outperforms Feedback Alignment (FA). The comparison is performed on only two data sets and each algorithm is better on one. Could the authors report results on at least two more data sets (however small or simple) during the rebuttal?
 * Fig and Table 1 report the same outcome. One of the two need to be removed.
Further Questions:
 * The Conv Net illustrated in Fig 2 panel A shares its weights with the biologically plausible net on panel B. Further, these two nets communicate for pre-training. How does the paper then isolate the contribution of the biologically plausible net to the prediction accuracy from the vanilla ConvNet? What would happen if we trained only the LIF net without a contact with the conv net?
 * Eq. 1 proposes induction of symmetry to solve the weight transform. At the extreme, this regularizer would make W and Y identical, boiling down to  a vanilla artificial neural net, which the ML community already knows wella nd performs with excellence. Would not having the biologically  implausible artificial neural model as the extreme solution contradict with the goal of biologically plausible learning? This would in the end make one conclude that the biological brain only performs a broken gradient descent.
Overall, this is a decent piece of work with some potential. My initial vote is a weak reject, as I  am at present missing sufficient evidence that the improved symmetry properties introduced by the causal inference scheme also brings an accuracy improvement over the vanilla feedback alignment method. I am open to improve to an accept if this evidence is provided and my aforementioned concerns primarily on the role of ConvNet are properly addressed during rebuttal.
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Post-rebuttal: My only major concern was the lack of sufficient empirical evidence to support the idea. The updated version of the manuscript has properly addressed this issue by reporting results on additional data sets. The authors have also given enlightening clarifications to some of the open points I have raised earlier. Hence, I'm happy to increase my score.