- 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
ramseshk
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