def test_introduced_bias_reversed_sign(self) -> None:
cbn = CausalBayesianNetwork([("A", "D"), ("A", "Y")])
cbn.add_cpds(A=discrete_uniform([0, 1]), D=lambda a: 0, Y=lambda a: a)
assert introduced_total_effect(cbn, "A", "D", "Y") == pytest.approx(-1)
cbn.add_cpds(Y=lambda a: -a)
assert introduced_total_effect(
cbn, "A", "D", "Y", adapt_marginalized=True) == pytest.approx(-1)
def test_random_cpd_copy() -> None:
"""check that a copy of a random cpd yields the same distribution"""
cbn = CausalBayesianNetwork([("A", "B")])
cbn.add_cpds(
RandomCPD("A"),
FunctionCPD("B", lambda a: a),
)
cbn2 = cbn.copy()
assert cbn.expected_value(["B"], {}) == cbn2.expected_value(["B"], {})
def test_introduced_bias_reversed_sign(self) -> None:
cbn = CausalBayesianNetwork([("A", "D"), ("A", "Y")])
cbn.add_cpds(
UniformRandomCPD("A", [0, 1]),
FunctionCPD("D", lambda a: 0),
FunctionCPD("Y", lambda a: a),
)
assert introduced_total_effect(cbn, "A", "D", "Y") == pytest.approx(-1)
cbn.add_cpds(FunctionCPD("Y", lambda a: -a))
assert introduced_total_effect(
cbn, "A", "D", "Y", adapt_marginalized=True) == pytest.approx(-1)
def test_query_disconnected_components() -> None:
cbn = CausalBayesianNetwork([("A", "B")])
cbn.add_cpds(A=RandomCPD(), B=RandomCPD())
cbn.query(["A"], {}, intervention={
"B": 0
}) # the intervention separates A and B into separare components