def get_adjustment_set(graph: CausalGraph, treatment: str,
outcome: str) -> Dict[str, List[Set[str]]]:
return {
"backdoor": backdoor.get_adjustment_sets(graph, treatment, outcome),
"frontoor": frontdoor.get_adjustment_set(graph, treatment, outcome),
"do-calculus": do_calculus.get_adjustment_set(graph, treatment,
outcome)
}
def test_canonical_frontdoor(self):
model_str = SAMPLE_DAGS["canonical_frontdoor"]
model = parse_model_string(model_str)
adj_sets = get_adjustment_sets(model)
# front door can't be identified using backdoor adjustment
assert len(adj_sets) == 0
def test_schrier_and_platt_2008(self):
model_str = SAMPLE_DAGS["Shrier&Platt, 2008"]
model = parse_model_string(model_str)
adj_sets = get_adjustment_sets(model)
assert len(adj_sets) == 7
def test_big_m(self):
model_str = SAMPLE_DAGS["big-M"]
model = parse_model_string(model_str)
adj_sets = get_adjustment_sets(model)
assert len(adj_sets) == 4
assert all("Z₁" in s for s in adj_sets)
def test_m_bias(self):
model_str = SAMPLE_DAGS["M-bias"]
model = parse_model_string(model_str)
adj_sets = get_adjustment_sets(model)
assert len(adj_sets) == 1
assert adj_sets[0] == set() # no adjustment must be performed
def test_simple_collider_backdoor(self):
model = parse_model_string(["x[T]->y[O]", "w->x", "w->y"])
adj_sets = get_adjustment_sets(model, treatment="x", outcome="y")
assert adj_sets == [set("w")]