IPW ATE Estimate

Problem

Estimate the average treatment effect with inverse propensity weighting: mean(t*y/e - (1-t)*y/(1-e)), clipping propensities to [1e-6, 1 - 1e-6].

Empty input returns 0.0.

Hint

Each unit stands in for the population its propensity implies.

case 1

in: [[1,1,0,0],[1,0,1,0],[0.5,0.5,0.5,0.5]]

expected: 0

case 2

in: [[1,1,0,0],[1,1,0,0],[0.5,0.5,0.5,0.5]]

expected: 1

case 3

in: [[1,0],[2,1],[0.8,0.5]]

expected: 0.25

case 4

in: [[],[],[]]

expected: 0

Starter codePython

def ipw_ate(treatment, outcome, propensity):
    # Your code here
    pass

4 test cases · run your code against them in the editor