Issue associated with Causal Forest DML when calling Score() method

[Fan981230]

Hi, thanks for the fascinating work you guys provide!

I'm new to causal inference field and i found this package super to help build model. While i came across an IndexError when trying to call the model.score() as i built a causal forest model.

The model parameter i used is as following:

model = CausalForestDML(
                model_y=RandomForestRegressor(n_estimators=150),
                model_t=RandomForestRegressor(n_estimators=150),
                n_jobs=-1,
                min_var_fraction_leaf=0.5,
                min_var_leaf_on_val=True,
                mc_iters = 4,
                n_estimators=50,
                subforest_size=5
                )
            
        model.tune(Y=Y, T=T, X=X, W=W)
        model.fit(Y=Y, T=T, W=W, X=X, cache_values=False)

And when i call the score method i received the following error:

And my guess for why it happened is the mc_iter parameter, as when i changed the mc_iters = 2, i can run the score method without any error. Really appreciate any advice or help, Thanks!

[kbattocchi]

Thanks for reporting this, and sorry for the inconvenience, this certainly looks like a bug, and is not limited to CausalForestDML. We'll take a look.

[Fan981230]

Thanks Keith! May i ask what might be the cause for the bug? is it when we apply multiple mc_iters the model is passing multiple result to the method that brings the index error?

[kbattocchi]

The nuisances are indexed by both mc_iters and the number of cross-fitting folds, and the logic we use to index into them here isn't correct

EconML/econml/_ortho_learner.py

Line 902 in 8b7fe33

nuisances[it][i * n_iters + j] = nuis

because i and j should be reversed (or n_splits should be used instead of n_iters)