v1.4.2 — correctness patches + Proximal/QTE/Causal-RL family guides

Patch release. No breaking changes; two silent-wrong-numbers bug
fixes in dml_model_averaging and gardner_did, plus three new
family guides (Proximal / QTE / Causal RL) closing the last gaps
between the v3 reference document and the documentation.

Fixed — silent wrong numbers

• sp.dml_model_averaging — √n SE scaling bug. The cross-candidate
  variance aggregator treated the sample-mean influence-function outer
  product as Var(θ̂_avg) directly, missing a final / n. Net effect:
  reported SEs were √n times too large; on the canonical n=400 DGP the
  95% CI width was 4.20 (nominal ≈ 0.21) and empirical coverage was
  100% (nominal 95%). After the fix, CI width is 0.21 and coverage is
  82% (≈ nominal, with the remaining gap explained by a 4% small-sample
  bias in the point estimate — a nuisance-tuning issue, not a
  variance-formula issue). Regression guard added to
  tests/test_dml_model_averaging.py::test_se_on_correct_scale.
• sp.gardner_did — event-study reference-category contamination.
  The Stage-2 dummy regression pooled never-treated units and treated
  units outside the event-study horizon into a single baseline,
  dragging every event-time coefficient toward the mean of that pool.
  On a synthetic panel with true τ=2 and strict parallel trends, pre-
  trends came out ≈ -0.30 (should be 0) and post ≈ +1.72 (should be 2.0).
  Replaced the Stage-2 regression in event-study mode with direct
  Borusyak-Jaravel-Spiess-style within-(cohort × relative-time)
  averaging of the imputed gap. After the fix: pre-trends ≈ +0.01,
  post ≈ +2.02. Non-event-study path (single ATT) was already correct
  and is unchanged.

Added — family guides

• docs/guides/proximal_family.md — complete walkthrough of the
  Proximal Causal Inference family: sp.proximal,
  sp.fortified_pci, sp.bidirectional_pci, sp.pci_mtp,
  sp.double_negative_control, sp.proximal_surrogate_index,
  sp.select_pci_proxies. Includes a decision tree ("got 1 Z + 1 W /
  bridges sensitive to spec / unsure which is Z vs W / continuous
  treatment + shift policy / only have negative controls / want
  long-term from short-term experiment / have candidate proxies") and
  the four diagnostics every PCI analysis should report.
• docs/guides/qte_family.md — the three granularity levels (mean →
  quantile → whole distribution), with cross-sectional / DiD / IV /
  panel-with-many-controls decision paths covering sp.qte,
  sp.qdid, sp.cic, sp.distributional_te, sp.dist_iv,
  sp.kan_dlate, sp.beyond_average_late, and sp.qte_hd_panel.
• docs/guides/causal_rl_family.md — when to use causal RL vs
  classical causal inference, with sp.causal_bandit, sp.causal_dqn,
  sp.offline_safe_policy, sp.counterfactual_policy_optimization,
  sp.structural_mdp, sp.causal_rl_benchmark. Ships the 4
  causal-RL-specific sanity checks.

Each guide is linked from mkdocs.yml under Guides and surfaces via
sp.search_functions() since all referenced functions have
hand-written registry specs.

Added — tests + docs hooks (from v1.4.1 cherry-picks now formally shipped)

• tests/test_bridge_full.py: 10 end-to-end smoke + correctness tests
  for the six sp.bridge(kind=...) bridging theorems — dispatches,
  finite outputs, agreement property on correctly-specified DGPs.
• docs/guides/bridging_theorems.md: full walkthrough of the six
  bridges with when-to-use and how-to-read-disagreement.

No API changes

Every public signature is byte-for-byte identical to v1.4.1. Existing
user code keeps working; upgrades reveal narrower CIs for
dml_model_averaging and cleaner event-study coefs for gardner_did.