SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure 5b shows the result on Erdős-Rényi graphs, where we vary the number of targets in  $\mathcal{I}^*$  on DAGs with 50 nodes. Figure 10a and Figure 10b show similar results on random tree graphs and Barabási-Albert graphs. In Figure 10c, we consider Erdős-Rényi graphs where  $|\mathcal{I}^*|$  is set to 25. This result shows the trend of varying number of nodes. We observe that our method is empirically competitive with the state-of-the-art method CliqueTree across all cases.
(a) Random tree graphs
Figure 10: Meek separator for causal matching. Each dot is averaged across 10 DAGs, where the error bar shows 0.2 standard deviation
(b) Barabási-Albert graphs
(c)  $|\mathcal{I}^*| = 25$