Propensity Trimming Fraction

Problem

Compute the fraction of propensity scores that need trimming: those strictly below epsilon or strictly above 1 - epsilon. Empty input returns 0.0.

Hint

Extreme propensities produce enormous weights, so samples near 0 or 1 are often dropped.

case 1

in: [[0.5,0.1,0.95,0.9],0.15]

expected: 0.75

case 2

in: [[0.5,0.5],0.1]

expected: 0

case 3

in: [[],0.1]

expected: 0

case 4

in: [[0,1],0]

expected: 0

case 5

in: [[0.05,0.95],0.1]

expected: 1

Starter codePython

def trimmed_propensity_fraction(propensities, epsilon):
    # Your code here
    pass

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