OrthoForest spend days working without result

[juandavidgutier]

Hello everyone,

I am running OrthoForest from DoWhy but it is running for days without result. I get this message:

"2022-01-16 20:57:41.811540: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found 2022-01-16 20:57:41.811571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine."

Any idea how to solve it?

Here is the dataset: new_incidence_sd_BC_test2_ml.csv

This is my code:

`

importing required libraries

import os, warnings, random import dowhy import econml from dowhy import CausalModel import pandas as pd import numpy as np

data = pd.read_csv("new_incidence_sd_BC_test2_ml.csv") #data_inf = data_inf.astype({"treatment":'bool'}, copy=False) data = data.dropna()

#data['treatment'] = data['treatment']*1000000000 #micrograms data_inf = data.drop(['X2','X3','X4'], axis=1) data_inf.head()

#Step 1: Modeling the causal mechanism #model with acumm PM2.5 as treatment, GEI and Temperatura(W) as common causes, Rainfall_accum2s as instrument model_inf=CausalModel( data = data_inf, treatment=['treatment'], outcome='y', instruments=['Z1', 'Z2'], common_causes=['W1', 'W2'], #common_causes=data_inf[['W1','W2']].to_numpy().reshape(-1, 2), #effect_modifiers=Xs.split('+'), effect_modifiers= ['X1'], graph= "digraph {W1->treatment;W1->y;W2->treatment;W2->y;Z1->treatment;Z2->treatment;W2->W1;treatment->y;X1->y;}" )

#view model model_inf.view_model()

#Step 2: Identifying effects identified_estimand = model_inf.identify_effect(proceed_when_unidentifiable=True) print(identified_estimand)

#Step 3: Estimation of the effect #with OrthoForest from sklearn.ensemble import GradientBoostingRegressor from sklearn.linear_model import LassoCV from econml.orf import DMLOrthoForest from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper

#step 3: Estimation of the effect orf_estimate_bd = model_inf.estimate_effect(identified_estimand, #test_significance=True, confidence_intervals=True, method_name="backdoor.econml.orf.DMLOrthoForest", method_params={ 'init_params': {'n_trees':250, 'min_leaf_size':20, 'max_depth':10, #'bootstrap':True, 'lambda_reg':0.01, 'random_state':123, 'model_Y':GradientBoostingRegressor(), 'model_T': GradientBoostingRegressor(), 'model_T_final':None, #WeightedLassoCVWrapper(cv=3), 'model_Y_final':None,}, #WeightedLassoCVWrapper(cv=3), 'fit_params': {} })

print(orf_estimate_bd) `