Add TransferCausalModel: causal forest for transfer effect estimation

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- TransferCausalModel(BaseModel): estimates causal effect of roster changes
  on team performance using a causal forest (Athey et al., 2019)
- Dual backend: econml.grf.CausalForest (preferred) or pure sklearn fallback
  with honest estimation (split on half, estimate on other half)
- predict_effect(): ATE, CATE, and 95% confidence intervals
- predict_individual_effect(): net effect of adding a player to roster
- rank_transfers(): rank candidate pool by predicted causal effect
- analyze_confounders(): identify features that confound transfer effect
- subgroup_effects(): estimate treatment effect by subgroup
- _build_roster_features(): compute roster-level features (role counts,
  points, minutes, age, formation entropy, interaction level)
- _build_player_features(): treatment features (projected points, role,
  scarcity, value over replacement)
- AuctionEffectAnalyzer: adjusts auction bids based on causal effects

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ramseshk

2026-08-11 17:21:43 +08:00

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commit f83a521649

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src/models/causal_forest.py

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