Treatment in balance library for sample and target population

[facebookresearch]

Treatment in balance library for sample and target population

[talgalili]

Hey @sde2ds
Almost.
You can see the code for ipw here:

balance/balance/weighting_methods/ipw.py

Line 506 in 7ad4937

y0 = np.concatenate((np.zeros(sample_n), target_weights * odds))

You can think of target (population) as 0 and sample (panel) as 1. In practice, it's creating a 2D array that has weights instead of 1s.

Feel free to open the thread if you have more questions. And also to share any work you do with the package.

[sde2ds]

Hi @talgalili Thanks for your response. In the above reply you mean target as 1 not treatment. Am i right?
Sure, will share some work in coming days

[talgalili]

Sorry, I wrote the wrong terms in the previous message.

In the context of survey statistics, it's target (or population) and sample (or panel).
In casual inference it's often treatment and control. How to match the terms is your choice.

In 'balance', target is 0 and sample is 1.
(I'll fix my previous message)