Binary treatment and Continuous outcome
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Description
In the case of Binary treatment[1 for treatment group 0 for control group] and Continuous outcome,
CASE1 : discrete_treatment=True
est = CausalForestDML(criterion='het')
set parameters for causal forest
est = CausalForestDML(criterion='het', random_state=1,
discrete_treatment=True,
honest=True,
inference=True,
cv=2,
model_t=LogisticRegressionCV(),
model_y=Lasso()
)
CASE2 : discrete_treatment=False
est = CausalForestDML(criterion='het', random_state=1,
discrete_treatment=False,
honest=True,
inference=True,
cv=2,
model_t=Lasso(),
model_y=Lasso()
)
CASE1 and CASE 2 basically work same function ?
In this case, which one is more fir between CASE 1 or CASE 2 ?
I wonder discrete_treatment=TRUE is applies for only multiple treatment not binary treatment.
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Research direction
No file or test is named. Start by reading the CausalForestDML documentation for discrete_treatment and the shown LogisticRegressionCV and Lasso configurations, then compare the binary-treatment cases. Done means documenting whether discrete_treatment applies to binary treatment and which configuration is appropriate.
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Assessment
- Tech stack
- machine-learning, python, scikit-learn
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100