Calculation of confidence intervals in NormalInferenceResults becomes very slow when passing big dataframes about econml HOT 2 CLOSED

import scipy
import timeit
import numpy as np

from time import perf_counter
from contextlib import contextmanager

size = 10000

@contextmanager
def catchtime() -> float:
    start = perf_counter()
    yield lambda: perf_counter() - start
    print(f'Time: {perf_counter() - start:.3f} seconds')

loc_array = np.random.normal(loc=0, scale=0.1, size=size)
scale_array = np.abs(np.random.normal(loc=0, scale=0.001, size=size))

print('Method without loop')
with catchtime() as t:
  a = scipy.stats.norm.ppf(q=0.05, loc=loc_array, scale=scale_array), scipy.stats.norm.ppf(q=0.95, loc=loc_array, scale=scale_array)

print(a)

print('Method with loop')
with catchtime() as t:
  b = np.array([scipy.stats.norm.ppf(q=0.05, loc=loc, scale=scale) for loc, scale in zip(loc_array, scale_array)]), np.array([scipy.stats.norm.ppf(q=0.95, loc=loc, scale=scale) for loc, scale in zip(loc_array, scale_array)])

print(b)