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The State of the Art in Causal Modeling
Causal modeling and causal machine learning have made progress, but there is still work to be done. Current methods can effectively handle small-scale problems and generate causal graphs. However, for larger problems like understanding cellular behavior, these methods aren't sufficient. Scaling up requires new algorithms and more computational power. Despite these challenges, many researchers are motivated to unlock the secrets of biology and find new therapies.