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Add experimental missing data support for GCM #1304

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@bloebp bloebp commented Mar 14, 2025

The current implementation has the following limits:

  • Only missing data in numerical features are supported
  • Auto assignment only considers non-linear models for missing data
  • Auto assignment only considers 3 different model types
  • Not all GCM methods are supported. For example, distribution change based on missing data does not work due to the independence tests.
  • Not tested thoroughly

To use the data with missing values, experimental_allow_nans has to be set to True in the assign_causal_mechanisms method. Manual model assignment should work as well.

Addresses: #1300

The current implementation has the following limits:
- Only missing data in numerical features are supported
- Auto assignment only considers non-linear models for missing data
- Auto assignment only considers 3 different model types
- Not all GCM methods are supported. For example, distribution change based on missing data does not work due to the independence tests.
- Not tested thoroughly

To use the data with missing values, experimental_allow_nans has to be set to True in the assign_causal_mechanisms method. Manual model assignment should work as well.

Signed-off-by: Patrick Bloebaum <[email protected]>
@bloebp bloebp requested a review from amit-sharma March 14, 2025 23:46
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