Hello, how did CausalVAE intervention operation? In the published source code, intervention is to forcibly assign a concept to a fixed integer by using "mask_z", as follows:

z_mask = torch.ones(q_m.size()[0], self.z1_dim, self.z2_dim).to(device) * adj       
decode_m[:, mask, :] = z_mask[:, mask, :]                       
decode_v[:, mask, :] = z_mask[:, mask, :]    

In the test result, the result is very poor. Where did I make a mistake?

The original input image is this

Output image with intervention of 1 for the 0th concept

Should I modify the published code to achieve the effect in the paper?

I download the CAM_1.0.tar.gz and run the setup_CAM.py, but when check if CAM and mboost have been installed, get 'need to install CAM and mboost'. I can't install CAM successfully.

In CausalVAE code Is there any special reason why using 4 different decoders?

Besides, for pendulum and flow datasets, the latent space dimension should be equal to the dimension of the label, which is 4. Why the latent space dimension in code is set to be 16?

I checked the Appendix of the paper: we extend the multivariate Gaussian to the matrix Gaussian. Why the model is designed in this way and what are the advantages if we set the VAE in this way?

Many thanks for your response

On the synthetic dataset, there is no error in the execution result. On the sachs dataset, the code error is as follows: Traceback (most recent call last): File "main.py", line 314, in main() File "main.py", line 284, in main graph_batch_pruned = np.transpose(pruning_cam(training_set.inputdata, np.array(graph_batch).T)) File "D:\code\trustworthyAI-master\Causal_Structure_Learning\Causal_Discovery_RL\src\helpers\cam_with_pruning_cam.py", line 129, in pruning_cam X2 = numpy2ri.py2rpy(XX) AttributeError: module 'rpy2.robjects.numpy2ri' has no attribute 'py2rpy' Please help me how to solve this problem？

When experimenting with the real dataset Sachs, the real dataset has been downloaded from the website, should I get the DAG.npy and data.npy of the real dataset?

Hi,

First of all, thank you for all your work on this package, collecting together the distributed (and sometimes un-implemented) research on causal inference is infinitely helpful and useful.

I was just wondering if you had any rough ETA on the implementation of the HPC algorithm within your toolbox? The results of this paper look extremely promising and I would like to test them further.

Many thanks

Hi,

I think it would be great to add GES algorithm implementation to gcastle. It would make broad comparisons between algorithms easier.

There is an existing Python implementation of GES by Juan Gamella: https://github.com/juangamella/ges

Maybe it could be integrated into gcastle. What are your thoughts?

If you think it's a good idea, I am happy to help with the integration.

BTW., I'll be speaking about gcastle in my upcoming conference talk next week: https://ghostday.pl/#agenda

Hi I already installed gcastle with pip install gcastle==1.0.3rc2. But when I try to run the demo program like anm_demo.py. There still an error said from castle.common import GraphDAG ModuleNotFoundError: No module named 'castle'

Anyone know why there is a ModuleNotFoundError even I have installed the package already?

Bumps tensorflow from 1.13.1 to 2.5.1.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hello! Thank you for the the implementation of the method proposed in paper published in ICLR2020. I have some questions when reproducing the experimental results.

Our commands is the same as the 'exp3' in README.md . And the data we used comes from 'https://github.com/kurowasan/GraN-DAG/blob/master/data/data_p10_e40_n1000_GP.zip', which should be the same as yours. But the performance is less than the results presented in the paper.

I'd appreciate it if you could tell me where the problem is.Thanks~

# Our command:
python main.py --max_length 10 --data_size 1000 --score_type BIC --reg_type GPR --read_data  --normalize --data_path ./data/data_p10_e40_n1000_GP_seed1 --lambda_flag_default --nb_epoch 20000 --input_dimension 128 --lambda_iter_num 1000

# training log:
2020-09-22 02:54:35,182 INFO - __main__ - [iter 18000] reward_batch: -4.175537586212158, max_reward: -3.6388002559621855, max_reward_batch: -3.7585916127016294
2020-09-22 02:56:12,809 INFO - __main__ - [iter 18500] reward_batch: -4.152896881103516, max_reward: -3.6388002559621855, max_reward_batch: -3.823972058605565
2020-09-22 02:58:37,596 INFO - __main__ - [iter 19000] lambda1 3.6388002559621855, upper 3.6388002559621855, lambda2 0.01, upper 0.01, score_min 3.6388002559621855, cyc_min 0.0
2020-09-22 02:58:40,635 INFO - __main__ - before pruning: fdr 0.7727272727272727, tpr 0.2564102564102564, fpr 5.666666666666667, shd 35, nnz 44
2020-09-22 02:58:40,636 INFO - __main__ - after  pruning: fdr 0.84375, tpr 0.1282051282051282, fpr 4.5, shd 37, nnz 32
2020-09-22 02:58:40,806 INFO - __main__ - [iter 19000] reward_batch: -4.204100608825684, max_reward: -3.6388002559621855, max_reward_batch: -3.7716216965236127
2020-09-22 03:00:17,209 INFO - __main__ - [iter 19500] reward_batch: -4.158496856689453, max_reward: -3.6388002559621855, max_reward_batch: -3.8702693792409377
2020-09-22 03:02:40,728 INFO - __main__ - [iter 20000] lambda1 3.6388002559621855, upper 3.6388002559621855, lambda2 0.01, upper 0.01, score_min 3.6388002559621855, cyc_min 0.0
2020-09-22 03:02:43,523 INFO - __main__ - before pruning: fdr 0.7727272727272727, tpr 0.2564102564102564, fpr 5.666666666666667, shd 35, nnz 44
2020-09-22 03:02:43,523 INFO - __main__ - after  pruning: fdr 0.84375, tpr 0.1282051282051282, fpr 4.5, shd 37, nnz 32
2020-09-22 03:02:43,733 INFO - __main__ - [iter 20000] reward_batch: -4.166861534118652, max_reward: -3.6388002559621855, max_reward_batch: -3.723014876623765
2020-09-22 03:02:45,956 INFO - __main__ - Model saved in file: output/2020-09-22_00-52-32-724/model/tmp.ckpt-20000
2020-09-22 03:02:45,957 INFO - __main__ - Training COMPLETED !

Hello! I have read all of your code and the great paper published in ICLR2020. I have some questions about the code: 1 . in train step1, why the loss1 of the actor has to be: self.loss1 = tf.reduce_mean(self.reward_baseline * self.log_softmax, 0) - 1 * self.lr1 * tf.reduce_mean(self.entropy_regularization, 0) i didn't find any formula or explanation in the paper. 2. What does reward base line in this code use for? I would be appreciated if you can answer these questions.

Hi!

I'd like to propose to add NPVAR algorithm (Gao et al., 2020) to the package. I believe that this would facilitate benchmarking and further research in the field.

There is an existing R implementation provided by the authors.

What are your thoughts on this idea?

References

Gao, M., Ding, Y., Aragam, B. (2020). A polynomial-time algorithm for learning nonparametric causal graphs.

Hi. When I use the GUI web for the task of data generation. I found that the edges in the graph are not equal to those in the configuration parameters. When I change the seed while keeping the n_nodes and n_edges identical, the edges in the graph may also be changed. So What's the effect of seed? Thanks.

Hello,

I am trying to use Causal Disocvery RL on bnlearn benchmarks. I encounter the error in convert_graph_int_to_adj_mat function.

The input to this function is

[-1903318164   235405414   101482606   495790951   201853294   378349935
 -1634426101 -1718146065   134742090          64   134742086   134742084
   446475107   470616428 -1785775892 -1768316434   201884524   134217728
   201949548 -1903613075   470286702   101187694 -1734505621   503843118
 -2070074547   134217838   513518542   503875886   235405386   445754223
           0      524358   236432367   134742086   134217792   134217792
   503908622]

And the error message follows:

Traceback (most recent call last):
  File "main.py", line 337, in <module>
    main()
  File "main.py", line 285, in main
    graph_batch = convert_graph_int_to_adj_mat(graph_int)
  File "/home/user/Causal_Discovery_RL/src/helpers/analyze_utils.py", line 156, in convert_graph_int_to_adj_mat
    for curr_int in graph_int], dtype=int)
  File "/home/user/Causal_Discovery_RL/src/helpers/analyze_utils.py", line 156, in <listcomp>
    for curr_int in graph_int], dtype=int)
ValueError: invalid literal for int() with base 10: '-'