CATE

class pqp.identification.CATE(outcome, treatment_condition, control_condition, subpopulation)

Bases: ATE

Causal estimand for the conditional average treatment effect

To define the conditional average treatment effect, it’s necessary to specify what is meant by treatment and control in this context, and you need to specify the subpopulation in which to measure the effect. You can do this by passing either a dict or a list of StatisticalEvent objects to each of the treatment_condition and control_condition arguments. If a dict is passed, the keys must be Variable or string, and the values must not be Variable. If a list is passed, it must contain only instances of StatisticalEvent.

Example

>>> # treatment condition is x = 1, control condition is x = 0 in both of these
>>  # in both, we are measuring the effect in the subpopulation where z = 1
>>> CATE(outcome, treatment_condition={"x": 1}, control_condition={"x": 0}, subpopulation={"z": 1})
>>> CATE(
...     outcome,
...     treatment_condition=[EqualityEvent("x", 1)],
...     control_condition=[EqualityEvent("x", 0)],
...     subpopulation=[EqualityEvent("z", 1)]
... )
>>>
>>> # treatment condition is x = 1 and y = "red", control condition is x = 0 and y = "blue"
>>> # we are measuring the effect in the subpopulation where z = 1
>>> CATE(
...     outcome,
...     treatment_condition={"x": 1, y: "red"},
...     control_condition={"x": 0, y: "blue"},
...     subpopulation={"z": 1}
... )

Parameters:

• outcome (Variable) – the outcome variable

• treatment_condition (dict or list) – the treatment condition

• control_condition (dict or list) – the control condition

• subpopulation (dict or list) – the subpopulation in which to measure the effect

Methods Summary

expression()

Derive the expression for the causal estimand

Methods Documentation

expression()

Derive the expression for the causal estimand

Returns:

the expression for the causal estimand

Return type:

AbstractExpression