ATE

class pqp.identification.ATE(outcome, treatment_condition, control_condition=None)

Bases: AbstractCausalEstimand

Causal estimand for the average treatment effect

To define the average treatment effect, it’s necessary to specify what is meant by “treatment” and “control” in this context. 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 str, 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
>>> ATE(outcome, treatment_condition={"x": 1}, control_condition={"x": 0})
>>> ATE(outcome, treatment_condition=[EqualityEvent("x", 1)], control_condition=[EqualityEvent("x", 0)])
>>>
>>> # treatment condition is x = 1 and y = "red", control condition is x = 0 and y = "blue"
>>> ATE(outcome, treatment_condition={"x": 1, y: "red"}, control_condition={"x": 0, y: "blue"})
Parameters:

  • outcome (Variable) – the outcome variable

  • treatment_condition (dict or list) – the treatment condition

  • control_condition (dict or list) – the control condition

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