SlowGuess/NeurIPS_2023_OCR huggingface dataset:
- Sub-optimum of greedy method. For the whole process of active causal discovery, the interventional data will be acquired successively in the greedy manner. Therefore, even if the strategy for acquisition is the optimal for each current step, the entire trajectory of causal discovery is sub-optimal. A possible solution is finding the best acquisition trajectory by reinforcement learning.
- Estimation of mutual information. For different circumstances, the costs, accuracy and data scale can be various. Therefore, the scale of mutual information can be affected as well. So it is important to adjust hyper-parameters accordingly.
- Scale of causal graph. It is a classic problem for causal discovery that most existing methods suffer from the difficulty in extending to large-scale graphs. The efficiency and effectiveness are supposed to be further improved, and we will optimize our model as well.
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