fix documentation

experiment_utils/experiment_analyzer.py

@@ -1,32 +1,5 @@
 """
 ExperimentAnalyzer class to analyze and design experiments
-
-Parameters
-----------
-data : DataFrame
-    PySpark Dataframe
-outcomes : List
-    List of outcome variables
-covariates : List
-    List of covariates
-treatment_col : str
-    Column name for the treatment variable
-experiment_identifier : List
-    List of columns to identify an experiment
-adjustment : str, optional
-    Covariate adjustment method (e.g., IPW, IV), by default None
-target_ipw_effect : str, optional
-    Target IPW effect (ATT, ATE, ATC), by default "ATT"
-propensity_score_method : str, optional
-    Propensity score method (logistic, xgboost), by default 'logistic'
-assess_overlap : bool, optional
-    Assess overlap between treatment and control groups (slow) when using IPW to adjust covariates, by default False
-instrument_col : str, optional
-    Column name for the instrument variable, by default None
-alpha : float, optional
-    Significance level, by default 0.05
-regression_covariates : List, optional
-    List of covariates to include in the final linear regression model, by default None
 """
 
 from typing import Dict, List, Optional, Tuple, Union
@@ -46,6 +19,33 @@
 class ExperimentAnalyzer:
     """
     Class ExperimentAnlyzer to analyze and design experiments
+
+    Parameters
+    ----------
+    data : DataFrame
+        PySpark Dataframe
+    outcomes : List
+        List of outcome variables
+    covariates : List
+        List of covariates
+    treatment_col : str
+        Column name for the treatment variable
+    experiment_identifier : List
+        List of columns to identify an experiment
+    adjustment : str, optional
+        Covariate adjustment method (e.g., IPW, IV), by default None
+    target_ipw_effect : str, optional
+        Target IPW effect (ATT, ATE, ATC), by default "ATT"
+    propensity_score_method : str, optional
+        Propensity score method (logistic, xgboost), by default 'logistic'
+    assess_overlap : bool, optional
+        Assess overlap between treatment and control groups (slow) when using IPW to adjust covariates, by default False
+    instrument_col : str, optional
+        Column name for the instrument variable, by default None
+    alpha : float, optional
+        Significance level, by default 0.05
+    regression_covariates : List, optional
+        List of covariates to include in the final linear regression model, by default None
     """
 
     def __init__(