SURE - Discovering causal structure#385
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Co-authored-by: Michael Foster <13611658+jmafoster1@users.noreply.github.com>
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This is looking good so far!
Jake248Newman
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Co-authored-by: Michael Foster <13611658+jmafoster1@users.noreply.github.com>
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- Coverage 97.89% 97.21% -0.69%
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- Misses 33 48 +15
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…ubset of variables from the data
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| The tool supports various configurations to tailor the discovery process to your specific dataset and domain knowledge: | ||
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| * **Fitness Functions** Choose between score-based and multi-objective fitness functions. |
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I've dropped out the ability to specify different fitness functions on the Hill Climber, since what I was actually thinking of was the multi-objective NSGA that I've now added.
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| * **Fitness Functions** Choose between score-based and multi-objective fitness functions. | ||
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| * **Search Constraints (Iterations)** |
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Probably worth documenting each technique and its parameters separately here. We can potentially use :autoclass for this, like I did here
| * **Excluding Edges:** If domain knowledge dictates that ``max_doses`` has no causal effect on any outputs, you | ||
| can specify ``max_doses -> ".*"`` and ``".*" -> max_doses`` in the *exclude edges* dot file. | ||
| * **Including Edges:** If it is known that ``vaccine`` directly affects all three outputs, you can specify | ||
| ``vaccine -> "cum_.*"`` in the *include edges* dot file. No newline at end of file |
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It'd also be good to include the exact command to run to get the DAG (and possibly a quick tutorial IPYNB) in tutorials/
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