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"""Lin, Ni & Lu (2023) §4.2 E-value sensitivity for a matched RMST difference.

Propensity-score matching balances *measured* covariates. An unmeasured
confounder ``U`` can still bias the matched ATT. The E-value answers a
single question:

    "How strong would U's association with treatment *and* U's association
     with the outcome each have to be, on a risk-ratio / mean-ratio scale,
     for that confounder to fully explain away the observed effect?"

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import os
from setuptools import find_packages, setup
## setup.py

def main():
    def read(fname):
        with open(os.path.join(os.path.dirname(__file__), fname)) as _in:
            return _in.read()

    setup(

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class Database(object):
    def _print(self):
        print('Database class')

class Local(object):
    def _print(self):
        print('Local class')

class BaseThing(object):
    def __init__(self):

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class MutableClass():
    def __init__(self):
        self.thing = [] # mutable
        self.nonething = None

    def fn(self, new):
        self.thing.append(new) 
        self.nonething = new

m, m2 = MutableClass(), MutableClass()

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# https://docs.python.org/3/library/unittest.mock.html
from unittest.mock import MagicMock, Mock
from nose.tools import assert_equal, raises, assert_true, assert_raises

class A():
    def __init__(self):
        self.item = 'item'

    def return_item(self, thing = None):
        if not thing:

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