Correct the continuous-treatment target transformation - #490
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Summary
Why this matters
For continuous treatment, the normalizing term is v(x) = Var(T | X=x), not one observed squared residual. The latter turns the pseudo-outcome into an unstable ratio whose unweighted conditional mean is generally not the treatment effect. The revised derivation gives a conditionally unbiased target under a conditionally linear response, explains the equivalent weighted R-loss, and distinguishes the special randomized example from the general case. Constant conditional treatment variance guarantees unchanged ranking when the denominator is omitted; when variance changes with X, the ranking may change unless additional structure preserves it.
For a linear-in-parameters least-squares stage, multiplying design rows and targets by square-root weights is algebraically equivalent to WLS. Applying the same transformation before a nonlinear learner changes its inputs and hypothesis class, so it is not a generic substitute for sample weights.
The nonlinear-treatment section also distinguishes elasticity from a marginal effect and notes that a zero-cost isoelastic revenue curve has no unconstrained interior optimum.
Validation
Closes #350
Closes #402
Closes #471