# Buggy code
def astype(self, dtype, copy=True):
    """
    Cast to a NumPy array or IntegerArray with 'dtype'.

    Parameters
    ----------
    dtype : str or dtype
        Typecode or data-type to which the array is cast.
    copy : bool, default True
        Whether to copy the data, even if not necessary. If False,
        a copy is made only if the old dtype does not match the
        new dtype.

    Returns
    -------
    array : ndarray or IntegerArray
        NumPy ndarray or IntergerArray with 'dtype' for its dtype.

    Raises
    ------
    TypeError
        if incompatible type with an IntegerDtype, equivalent of same_kind
        casting
    """

    # if we are astyping to an existing IntegerDtype we can fastpath
    if isinstance(dtype, _IntegerDtype):
        result = self._data.astype(dtype.numpy_dtype, copy=False)
        return type(self)(result, mask=self._mask, copy=False)

    # coerce
    if is_float_dtype(dtype):
        # In astype, we consider dtype=float to also mean na_value=np.nan
        kwargs = dict(na_value=np.nan)
    else:
        kwargs = {}

    data = self.to_numpy(dtype=dtype, **kwargs)
    return astype_nansafe(data, dtype, copy=False)


# Traceback (ValueError)
In [23]: a = pd.array([1, 0, pd.NA])

In [24]: a
Out[24]:
<IntegerArray>
[1, 0, <NA>]
Length: 3, dtype: Int64

In [25]: a.astype("boolean")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-25-41973ed53ee3> in <module>
----> 1 a.astype("boolean")

~/scipy/pandas/pandas/core/arrays/integer.py in astype(self, dtype, copy)
454             kwargs = {}
455
--> 456         data = self.to_numpy(dtype=dtype, **kwargs)
457         return astype_nansafe(data, dtype, copy=False)
458

~/scipy/pandas/pandas/core/arrays/masked.py in to_numpy(self, dtype, copy, na_value)
124             ):
125                 raise ValueError(
--> 126                     f"cannot convert to '{dtype}'-dtype NumPy array "
127                     "with missing values. Specify an appropriate 'na_value' "
128                     "for this dtype."

ValueError: cannot convert to 'boolean'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.

In [26]: a.astype(pd.BooleanDtype())
...
ValueError: cannot convert to 'boolean'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.

# Fixed code
def astype(self, dtype, copy=True):
    """
    Cast to a NumPy array or IntegerArray with 'dtype'.

    Parameters
    ----------
    dtype : str or dtype
        Typecode or data-type to which the array is cast.
    copy : bool, default True
        Whether to copy the data, even if not necessary. If False,
        a copy is made only if the old dtype does not match the
        new dtype.

    Returns
    -------
    array : ndarray or IntegerArray
        NumPy ndarray or IntergerArray with 'dtype' for its dtype.

    Raises
    ------
    TypeError
        if incompatible type with an IntegerDtype, equivalent of same_kind
        casting
    """
    from pandas.core.arrays.boolean import BooleanArray, BooleanDtype

    dtype = pandas_dtype(dtype)

    # if we are astyping to an existing IntegerDtype we can fastpath
    if isinstance(dtype, _IntegerDtype):
        result = self._data.astype(dtype.numpy_dtype, copy=False)
        return type(self)(result, mask=self._mask, copy=False)
    elif isinstance(dtype, BooleanDtype):
        result = self._data.astype("bool", copy=False)
        return BooleanArray(result, mask=self._mask, copy=False)

    # coerce
    if is_float_dtype(dtype):
        # In astype, we consider dtype=float to also mean na_value=np.nan
        kwargs = dict(na_value=np.nan)
    else:
        kwargs = {}

    data = self.to_numpy(dtype=dtype, **kwargs)
    return astype_nansafe(data, dtype, copy=False)