EconML
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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 goal...
import dowhy import econml.dowhy est = LinearDML(model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor(), featurizer=PolynomialFeatures(degree=2, include_bias=False)) est_dw = est.dowhy.fit(Y, T, X=X, W=W, outcome_names=["log_demand"], treatment_names=["log_price"], feature_names=["income"], confounder_names=confounder_names, inference="statsmodels") ImportError: dowhy is no longer a dependency of the...
Hi, I am fitting a DML model to my data, and I know the ATE of my treatment is positive, but the model gives me the negative result. I am...
If multiple treatments are specified, a warning is given if a scalar is provided as treatment value for the effect estimation, e.g. `est.effect(X=X, T0=0, T1=1)`. https://github.com/py-why/EconML/blob/8d58f37acc06b9b8fb0c57f9d4494c46d8c24d74/econml/_cate_estimator.py#L855-L858 However, since this check...
We have a build failure over at dowhy when upgrading statsmodels due to > Note: This error originates from the build backend, and is likely not a problem with poetry...
See issue https://github.com/py-why/EconML/issues/786 for details.
See issue https://github.com/py-why/EconML/issues/786 for details.
In the case of Binary treatment[1 for treatment group 0 for control group] and Continuous outcome, CASE1 : discrete_treatment=True # est = CausalForestDML(criterion='het') # set parameters for causal forest est...
