v0.0.7

[0.0.7] - 2026-07-18

Added

• estimate_propensity_scores() estimates in-sample or out-of-fold treatment
  probabilities without rerunning the outcome model.
• summarize_propensity_scores() and plot_propensity_scores() report and
  visualize common support, tail mass, and inverse-weight effective sample size.
• LFC() now returns both clipped pi_hat and diagnostic pi_hat_raw scores.
• LFC() results include raw AIPW mean_control, mean_treated, and
  estimable diagnostics.
• Propensity and latent-factor association diagnostics in the Perturb-seq
  tutorial.

Changed

• estimate_propensity_scores() now defaults to class-balanced logistic
  weighting, matching LFC() and historical causarray behavior. Pass
  class_weight=None explicitly for calibrated treatment probabilities.

Fixed

• LFC() now always uses standard, unclipped AIPW pseudo-outcomes while
  retaining its established class-balanced propensity nuisance fit to limit
  unrelated result drift. This removes directional bias from clipping negative
  cell-level influence-function values under treatment-group imbalance.
  Calibrated scores remain available with ps_class_weight=None; the legacy
  cell-level clipping option has been removed.
• Valid aggregate counterfactual means are floored only for the final log-ratio
  and delta-method calculation, after averaging the unchanged pseudo-outcomes.
• Nonfinite or nonpositive aggregate counterfactual means are reported as
  non-estimable rather than silently floored before taking a log ratio.
• cross_est=True now enables two-fold nuisance cross-fitting, with an explicit
  K taking precedence.
• Intercept-only propensity models now respect the requested class weighting:
  balanced fits return 0.5 and unweighted fits return the case prevalence.
  Masks are indexed correctly within cross-fitting folds.