Formative

from formative.causal import DAG, OLSObservational
from formative.game import minimax, maximax, maximin
 
 
# ---- Causal estimation (with heterogeneous effects) ----
 
dag = DAG()
dag.assume("ability").causes("education", "income")
dag.assume("segment").causes("income")
dag.assume("education").causes("income")
 
result = OLSObservational(
    dag,
    treatment="education",
    outcome="income",
    effect_modifier="segment",     # effect estimated per segment
).fit(df)
print(result.summary())
 
 
# ---- Refutation ----
 
print(result.refute(df).summary())
 
 
# ---- Decisions ----
 
decision = result.decide(cost=2.5, benefit=1.0)
print(decision)
 
for level, d in result.decide_by_group(cost=2.5, benefit=1.0).items():
    print(level, "→", d.optimal)   # different segments
 
 
# ---- Decisions via game theory ----
 
outcomes = decision.to_outcomes()
print(minimax(outcomes).solve())   # pessimistic choice
print(maximax(outcomes).solve())   # high risk, high reward
print(maximin(outcomes).solve())   # best worst-case