Flip every signature to `(X, y, treatment, ...)` and delete the deprecation shim

[jeongyoonlee]

Part of #980 (v1.0 M1). Refs #854.

The breaking change itself: reorder every signature to (X, y, treatment, …) and delete the deprecation shim.

This lands as one change. Once a signature is reordered, existing positional calls break silently — y and treatment are both same-length arrays, so there is no TypeError to catch a swap. A partially flipped package is therefore worse than either end state, and no subset of this checklist may be released on its own.

Blocked by the window-closing issues on #980: the shim's coverage must be final (#981) and CausalML's own callers migrated (#982, #983) before anything moves.

The rule

X, then y, then treatment; every other parameter keeps its relative position. Already implemented as v1_order() in causalml/inference/_arg_order.py and derived per method from that method's own signature — so the target for each method is machine-readable, not hand-maintained. tests/test_fit_arg_order.py::test_v1_order pins it.

Checklist

• Meta-learners — BaseSLearner, BaseTLearner, BaseXLearner, BaseRLearner, BaseDRLearner and subclasses (XGBTRegressor, XGBRRegressor, XGBRClassifier, XGBTClassifier, BaseXClassifier, BaseRClassifier, LRSRegressor), plus bootstrap / fit_bootstrap_ensemble
• Causal trees and forests — CausalTreeRegressor (fit, fit_predict, estimate_ate, bootstrap, bootstrap_pool), CausalRandomForestRegressor
• Uplift trees and forests — UpliftTreeClassifier / _KernelUpliftTreeClassifier, UpliftRandomForestClassifier / _KernelUpliftRandomForestClassifier; resolve the X_val, treatment_val, y_val question from the epic
• IV — IVRegressor.fit → (X, y, treatment, w); BaseDRIVLearner.fit / fit_predict / estimate_ate / predict / bootstrap → (X, y, treatment, assignment, …)
• Neural estimators — TF and JAX DragonNet, Torch and JAX CEVAE
• Standalone — PolicyLearner, TMLELearner
• Update every Args: docstring block to match the new order
• Delete causalml/inference/_arg_order.py, the __init_subclass__ hook in causalml/inference/serialization.py, and the six @shim_arg_order class decorators
• Rewrite tests/test_fit_arg_order.py to assert the new order rather than the deprecation (the file's docstring says to update, not delete, these tests)

Acceptance

• inspect.signature(learner.fit).parameters starts X, y, treatment for every learner in the checklist.
• No FutureWarning machinery remains; grep for _arg_order, shim_arg_order, _in_arg_order_call is empty.
• Full suite green, and the equivalence test that guards against a silent y/treatment swap is retained in flipped form.