CausalForestDML SHAP values not working properly

Hello,

I am using econml version 0.10.0 and shap v. 0.39.0

When I calculate SHAP values for CausalForestDML estimator the results do not make sense, as opposed to when I calculate them the same way on other estimators like DRL/Meta-learners.

Here is a brief description of my issue:

Y = ['event_flag']
T = ['Diabetes_A1c_Test_compliance_category_Non-Compliant']

So I am able to access my shap values using: shap.plots.bar(shap_values[Y[0]][T[0]+'_1.0']) for non-CausalForestDML models, however, for CausalForestDML the shap values take a different form:

shap.plots.bar(shap_values['Y0']['T0_1.0']), even though everything else has been set up the same

Additonally, the results dont make sense:

For example, for DRL I see about what I expect: (clear directional relationships between the impact and the value of the feature, for the most part)

But for DML (built the same way, only difference is the estimator used), the results are not at all what I would expect: (very little directional relationships)

Thanks!

No source file or test is identified in the report. Start by reproducing the CausalForestDML case with the stated EconML and SHAP versions, then compare its SHAP value structure and plots with the DRL or meta-learner cases using shap.plots.bar. Done is not defined beyond making the CausalForestDML results behave as expected.