code: def debias_with_fixed_effects(
      df: pd.DataFrame,
      treatment_column: str,
      outcome_column: str,
      confounder_columns: List[str],
      suffix: str = "_debiased",
      denoise: bool = True,
  ) -> pd.DataFrame:
      """
      Returns a dataframe with the debiased columns with suffix appended to the name
  
      This is equivalent of debiasing with regression where the forumla is "C(x1) + C(x2) + ...".
      However, it is much more eficient than runing such a dummy variable regression.
  
      Parameters
      ----------
  
      df : Pandas DataFrame
          A Pandas' DataFrame with with treatment, outcome and confounder columns
  
      treatment_column : str
          The name of the column in `df` with the treatment.
  
      outcome_column : str
          The name of the column in `df` with the outcome.
  
      confounder_columns : list of str
          Confounders are categorical groups we wish to explain away. Some examples are units (ex: customers),
          and time (day, months...). We perform a group by on these columns, so they should not be continuous
          variables.
  
      suffix : str
          A suffix to append to the returning debiased column names.
  
      denoise : bool (Default=True)
          If it should denoise the outcome using the confounders or not
  
      Returns
      ----------
      debiased_df : Pandas DataFrame
          The original `df` dataframe with debiased columns added.
      """
  
      cols_to_debias = (
          [treatment_column, outcome_column]
          if denoise
          else [treatment_column]
      )
  
      def debias_column(c: str) -> dict:
          mu = sum(
              [
                  df.groupby(x)[c].transform("mean")
                  for x in confounder_columns
              ]
          )
          return {c + suffix: df[c] - mu + df[c].mean()}
  
      return df.assign(
          **merge(*[debias_column(c) for c in cols_to_debias])
      )
  
file_path: fklearn/src/fklearn/causal/debias.py
imports: from typing import List
  import numpy
  import pandas
  from sklearn.base import RegressorMixin
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import cross_val_predict
  from statsmodels.formula.api import ols
  from toolz import curry, merge
  from typing import Dict, Any
label_desc: Use image node_id 3 for calling a global function with example usage: debias_with_fixed_effects(df, treatment_column, outcome_column, confounder_columns, suffix, denoise) and returns: df, dict