Keith Battocchi

The partial linear regression specification is basically the same, except that they are not estimating a heterogeneous effect and the variable names differ: their D is our T, their X...

Without more information it's hard to give advice, but there is no way to constrain the output of the CausalAnalysis class to be positive. You could always post-process the results...

Simple reproduction: ``` from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression from sklearn.linear_model import LinearRegression import numpy as np X = np.random.normal(size=(100,5)).astype('O') y = np.random.normal(size=100) LinearRegression().fit(X,y) StatsModelsLinearRegression().fit(X,y) ```

Could you please include the output of `pip list`, as well as letting us know the version of python you're using? That error message means that if you want to...

I think the issue is that your version of keras is higher than we support; could you try installing `keras

I believe that this is the code that we're trying and failing to call that is resulting in that message: import keras from keras import backend as K import keras.layers...

Could you please include the full stack trace of the exception? Also, what if you just run the four lines I suggested in isolation in a new script, rather than...

By default, `LinearDML` has its `linear_first_stages` attribute set to `True`, which for technical reasons will cause the Y model to be trained on [X,W] cross [1;phi(X)], where phi is the...

Hi, could you please include the code that you were running when you saw this issue, the full stack trace, and the output of `pip list`? Thanks!

Generally our estimators support only a single discrete treatment (although many estimators do support an arbitrary cardinality for that that single treatment, not just binary). So if you have multiple...