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Correct the continuous-treatment target transformation - #490

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AliFozooni:agent/correct-continuous-treatment-transformation
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Correct the continuous-treatment target transformation#490
AliFozooni wants to merge 1 commit into
matheusfacure:masterfrom
AliFozooni:agent/correct-continuous-treatment-transformation

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@AliFozooni

@AliFozooni AliFozooni commented Aug 15, 2026

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Summary

  • derive the continuous-treatment transformed target using conditional treatment variance
  • explain why dividing by an individual squared residual is unstable and does not recover CATE
  • connect the normalized pseudo-outcome to the residual-on-residual R-loss
  • give a clean sufficient condition under which omitting variance preserves treatment-effect ranking
  • explain why square-root row scaling reproduces WLS only for linear least squares, not nonlinear learners
  • recommend native sample weights or direct R-loss optimization for nonlinear final stages
  • correct the treatment-variance notation and isoelastic-demand discussion

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

  • built the full Jupyter Book successfully after the final weighting and ranking clarifications
  • validated notebook JSON and checked the diff for whitespace errors
  • confirmed that no code cells or stored outputs changed

Closes #350
Closes #402
Closes #471

@AliFozooni
AliFozooni marked this pull request as ready for review August 15, 2026 18:06
@AliFozooni
AliFozooni force-pushed the agent/correct-continuous-treatment-transformation branch from d3d427a to 62a8cd7 Compare August 15, 2026 18:28
@AliFozooni
AliFozooni force-pushed the agent/correct-continuous-treatment-transformation branch from 62a8cd7 to 3e9fc6e Compare August 15, 2026 18:29
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