#1036: Fix S- and X-learner bootstrap confidence interval reproducibility

[su-jin1425]

Proposed changes

Fixes #1036.

S- and X-learner bootstrap confidence intervals were using the global NumPy random state, making results change when unrelated code consumed the global RNG.

This change:

• Adds random_state support to the affected S- and X-learner classes.
• Normalizes the seed with check_random_state.
• Passes the resulting RNG to the affected bootstrap paths.
• Ensures random_state is preserved through get_params() and clone().
• Adds regression tests for reproducible bootstrap confidence intervals.

The existing T-learner seeded bootstrap path and DR-learner behavior are preserved.

Before and After

Test

$ErrorActionPreference="Stop"; $tmp="$env:TEMP\causalml-1036"; if(Test-Path $tmp){Remove-Item $tmp -Recurse -Force}; git clone -q "https://github.com/uber/causalml.git" $tmp; Set-Location $tmp; git fetch -q "https://github.com/su-jin1425/causalml.git" "fix-issue-1036"; $env:PYTHONPATH=$tmp; Write-Host "`n========== BEFORE: 9a23278 ==========" -ForegroundColor Yellow; git checkout -q --detach 9a2327811406240925e9b8c0ce16734a04da52d7; @'
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from causalml.inference.meta.slearner import BaseSRegressor
from causalml.inference.meta.xlearner import BaseXRegressor
rng=np.random.RandomState(123); X=rng.normal(size=(80,3)); t=rng.randint(0,2,80); y=2*X[:,0]+t*(1+X[:,1])+rng.normal(size=80); p=np.full(80,t.mean())
def run(M,**kw):
    m=M(learner=RandomForestRegressor(n_estimators=5,random_state=7),**kw)
    return np.asarray(m.estimate_ate(X,y=y,treatment=t,p=p,bootstrap_ci=True,n_bootstraps=30,bootstrap_size=30)[1:])
def check(M,**kw):
    np.random.seed(123); a=run(M,**kw); np.random.rand(10000); b=run(M,**kw); return np.array_equal(a,b)
print("Commit:",__import__("subprocess").check_output(["git","rev-parse","--short","HEAD"],text=True).strip())
print("S reproducible:",check(BaseSRegressor))
print("X reproducible:",check(BaseXRegressor))
'@ | python; Write-Host "`n========== AFTER: 36ce8f2 ==========" -ForegroundColor Green; git checkout -q --detach 36ce8f24672186c4c0ddc23d4ed477d476edcc51; @'
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from causalml.inference.meta.slearner import BaseSRegressor
from causalml.inference.meta.xlearner import BaseXRegressor
rng=np.random.RandomState(123); X=rng.normal(size=(80,3)); t=rng.randint(0,2,80); y=2*X[:,0]+t*(1+X[:,1])+rng.normal(size=80); p=np.full(80,t.mean())
def run(M,**kw):
    m=M(learner=RandomForestRegressor(n_estimators=5,random_state=7),**kw)
    return np.asarray(m.estimate_ate(X,y=y,treatment=t,p=p,bootstrap_ci=True,n_bootstraps=30,bootstrap_size=30)[1:])
def check(M,**kw):
    np.random.seed(123); a=run(M,**kw); np.random.rand(10000); b=run(M,**kw); return np.array_equal(a,b)
print("Commit:",__import__("subprocess").check_output(["git","rev-parse","--short","HEAD"],text=True).strip())
print("S reproducible:",check(BaseSRegressor,random_state=123))
print("X reproducible:",check(BaseXRegressor,random_state=123))
print("Expected: BEFORE False/False | AFTER True/True")
'@ | python; Set-Location $PWD

Types of changes

• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation Update (if none of the other choices apply)

Checklist

• I have read the [CONTRIBUTING](https://github.com/uber/causalml/blob/master/CONTRIBUTING.md) doc
• I have signed the [CLA](https://cla-assistant.io/uber/causalml)
• Lint and unit tests pass locally with my changes
• I have added tests that prove my fix is effective or that my feature works
• I have added necessary documentation (if appropriate)
• Any dependent changes have been merged and published in downstream modules

Further comments

The fix uses the existing check_random_state approach established by #1035 and keeps the change limited to bootstrap random-state handling and its regression coverage.

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