docstr: Predict individual treatment effects
  
  Args:
      X (np.matrix): a feature matrix
      with_outcomes (bool), default=False,
                            include outcomes Y_hat(X|T=0), Y_hat(X|T=1) along with individual treatment effect
  Returns:
     (np.matrix): individual treatment effect (ITE), dim=nx1
                  or ITE with outcomes [Y_hat(X|T=0), Y_hat(X|T=1), ITE], dim=nx3
docstr_tok: ["Predict","individual","treatment","effects","Args",":","X","(","np.matrix",")",":","a","feature","matrix","with_outcomes","(","bool",")",",","default=False",",","include","outcomes","Y_hat","(","X|T=0",")",",","Y_hat","(","X|T=1",")","along","with","individual","treatment","effect","Returns",":","(","np.matrix",")",":","individual","treatment","effect","(","ITE",")",",","dim=nx1","or","ITE","with","outcomes","[","Y_hat","(","X|T=0",")",",","Y_hat","(","X|T=1",")",",","ITE","]",",","dim=nx3"]
code: def predict(
      self, X: np.ndarray, with_outcomes: bool = False
  ) -> np.ndarray:
      """Predict individual treatment effects
  
      Args:
          X (np.matrix): a feature matrix
          with_outcomes (bool), default=False,
                                include outcomes Y_hat(X|T=0), Y_hat(X|T=1) along with individual treatment effect
      Returns:
         (np.matrix): individual treatment effect (ITE), dim=nx1
                      or ITE with outcomes [Y_hat(X|T=0), Y_hat(X|T=1), ITE], dim=nx3
      """
      if with_outcomes:
          self.n_outputs_ = self.max_outputs_
          for estimator in self.estimators_:
              estimator._with_outcomes = True
      else:
          self.n_outputs_ = 1
      y_pred = super().predict(X)
      return y_pred
  
imports: from typing import Union
  import numpy
  import forestci
  from joblib import Parallel, delayed
  from warnings import catch_warnings, simplefilter, warn
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils.validation import check_random_state, _check_sample_weight
  from sklearn.utils.multiclass import type_of_target
  from sklearn import __version__
  from sklearn.ensemble._forest import DOUBLE, DTYPE, MAX_INT
  from sklearn.ensemble._forest import ForestRegressor
  from sklearn.ensemble._forest import compute_sample_weight, issparse
  from sklearn.ensemble._forest import _generate_sample_indices, _get_n_samples_bootstrap
  from .causaltree import CausalTreeRegressor
label_desc: Use image node_id 4 for calling the CausalRandomForestRegressor obj's underlying member method code with example usage: obj.predict(X, with_outcomes) and returns: y_pred