Causal Forest DML has very wide confidence interval

Below is my code to estimate treatment effects. There is a much wider confidence interval of ATT (i.e., [-200k, 900k]) by Causal Forest DML model, compared to that calculated by linear DML model (i.e, [200k, 400k]). Are there any ways to make CI by Causal Forest DML narrower, and ideally statistically significant ?

# Linear DML for the ATE
   dml = LinearDML(
       model_y = rf_reg,
       model_t = xgb_class, 
       discrete_treatment = True,
       random_state = 0,
       cv = StratifiedKFold(5))

   print('Fitting linear DML...')
   results_dml = dml.fit(Y=Y,T=T,W=X)
   ate = dml.intercept_
   ate_lb = dml.intercept__interval()[0]
   ate_ub = dml.intercept__interval()[1]
   
   print('DML ATE:', round(ate, 2), 'CI [', round(ate_lb, 2), ',', round(ate_ub, 2), ']', '$')

# Causal Forest for the ITE
   cf = CausalForestDML(
       model_y = rf_reg,
       model_t = xgb_class, 
       discrete_treatment = True,
       cv=StratifiedKFold(5),
       random_state = 0,
       n_estimators=300,
   )

   print('Fitting causal forest ...')
   results = cf.fit(Y=Y,T=T,X=X,cache_values=True)

   # ITE Estimates with lower and upper bound
   ite_estimates = cf.effect(X)
   lb_estimates, ub_estimates = cf.effect_interval(X)

   # Create DataFrame with individual ITE estimates, lower bound, and upper bound
   all_individual_effects_df = pd.DataFrame({
       'ITE': ite_estimates,
       'ITE_lb': lb_estimates,
       'ITE_ub': ub_estimates
   }, index=df.index)

   # Concatenate with other relevant data
   all_ITEs = pd.concat([df[['sfdc_customer_id']], T, Y, all_individual_effects_df], axis=1)
   
   # Calculate ATT for treatment group
   att = all_ITEs[all_ITEs[treatment_variable] == 1]['ITE'].mean()
   att_lb = all_ITEs[all_ITEs[treatment_variable] == 1]['ITE_lb'].mean()
   att_ub = all_ITEs[all_ITEs[treatment_variable] == 1]['ITE_ub'].mean()

   # Print ATT results
   print('CF ATT:', round(att, 2), 'CI [', round(att_lb, 2), ',', round(att_ub, 2), ']', '$')

No repository file or test is named. Start by reviewing CausalForestDML.effect_interval and the contrast with LinearDML.intercept__interval(), then reproduce the supplied fit configuration; done would require a documented cause or a maintainer-defined change.