How can I print out MSE at each hyper-parameter tuning training iteration?

py-why / EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

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Hello Keith, @kbattocchi I am trying to print the error/MSE at each iteration during train when tuning in auto mode?. I am using this code:

causal_forest = CausalForestDML(criterion='het',
                                discrete_treatment=False,
                                honest=True,
                                inference=True,
                                cv=8,
                                model_t=MultiTaskLassoCV(), 
                                model_y=MultiTaskLassoCV(),
                                )

final_model=causal_forest.tune(Y, T, X=X,W=W,**params='auto')**.fit(Y, T, X=X, W=W,cache_values=True).refit_final()

Can someone share the syntax please? Also, how do I get the optimal parameter once auto-tuning is completed?

py-why / EconML

How can I print out MSE at each hyper-parameter tuning training iteration? #703