ENH: treatment effect with covariates in randomized trials

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
data

Research direction

Start by reading the existing TreatmentEffect implementation referenced through PR #8034, then compare it with the semiparametric treatment-effect discussion in issue #2443. Review the cited Zhang, Tsiatis, and Davidian paper and determine the required API and estimation design for given selection probabilities. Done means an agreed implementation scope, supporting tests, and documented behavior for randomized trials with covariates.

Written by the indexing model from the issue text.

Description

comp-treatment type-enh

followup to semi-parametric treatment effect under ignorability or conditional independence
#2443 SUMM issue
#8034 PR for teffects

Main difference: selection probability is given and orthogonal to outcome explanatory variables X, e.g. random sampling
maybe similar to survey weights.

parking a reference, there are many related articles that I have not looked at

method looks a bit similar to AIPW, correction term to predicted mean (POM) of a baseline regression model
(no ipw weighting in regression)

Zhang, Min, Anastasios A. Tsiatis, and Marie Davidian. "Improving efficiency of inferences in randomized clinical trials using auxiliary covariates." Biometrics 64, no. 3 (2008): 707-715.

I guess it will follow mostly the same pattern as in new TreatmentEffect but without momcond for selection/treatment model, probs takes as given and exogenous.

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