Overview

BART-Survival is a high-performance Python package for discrete-time survival analysis using Bayesian Additive Regression Trees (BART). Combining the power and flexibility of PyMC probabilistic programming with specialized discrete-time hazard modeling, BART-Survival enables fully non-parametric time-to-event estimation, non-linear covariate interaction modeling, and causal effect estimation without requiring proportional hazards or linearity assumptions.

Key Highlights (2026 Release)

• Intuitive High-Level API (SurvBART): End-to-end scikit-learn compatible workflow with fit_survival(), predict_survival(), feature_importance(), and estimate_effect().
• Standardized Model Presets: Battle-tested presets (preset_standard(), preset_fast(), preset_high_precision()) calibrated for MCMC stability and rapid prototyping.
• Empirical Node Value Splitting: Proposes tree split cutpoints at exact empirical midpoints (up to 512 candidates per feature), capturing step-function risk dynamics with high fidelity.
• Support-Audited Causal G-Computation: Automated detection of conditional support and positivity violations (e.g., zero length-of-stay for outpatients) with conditional donor resampling to eliminate off-support extrapolation.
• Warm-Started DART Dirichlet Sparsity: Optional variable selection prior to identify true risk drivers in high-dimensional tabular cohorts.
• Fully Accessible Hyperparameters: Direct, open parameter exposure on SurvBART with property bindings and InferenceData serialization.
• Synthetic Survival Benchmarking: Built-in data generators (simulate_confounded_survival_data) for reproducible testing with known ground-truth hazard ratios.
• 100% Backward Compatible: Full compatibility with legacy BartSurvModel, get_surv_pre_train, and get_posterior_test pipelines.

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Installation

Install from PyPI:

pip install bart-survival

Or install the latest development branch locally:

git clone https://github.com/CDCgov/BART-Survival.git
cd BART-Survival
pip install -e .

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Quickstart

1. High-Level End-to-End Workflow

import numpy as np
import pandas as pd
from bart_survival import SurvBART, simulate_confounded_survival_data

# 1. Generate realistic synthetic cohort with known ground-truth treatment effect
df, meta = simulate_confounded_survival_data(
    N=400, 
    treatment_hr=1.75, 
    censoring_rate=0.25, 
    seed=42
)
feature_cols = ["inpatient", "age", "stage", "los"]
X = df[feature_cols]

# 2. Initialize SurvBART with standard scientific preset
# (50 trees, 1000 draws, 250 tune, 4 chains, thin=4, empirical 512 cutpoints)
model = SurvBART.preset_standard()

# 3. Fit directly from tabular covariates and time-to-event outcomes
model.fit_survival(
    X=X,
    time=df["time"],
    status=df["status"],
    time_scale=5.0,     # Discretize continuous time into 5-day intervals
    progressbar=True,
    random_seed=42
)

# 4. Predict cumulative survival curves S(t) with 95% Credible Intervals
surv_summary = model.predict_survival(X, return_quantiles=True)
print("Survival curve shape (subjects x times):", surv_summary["mean"].shape)
print("Mean S(t) at final interval:", surv_summary["mean"][:, -1].mean())

# 5. Extract normalized posterior feature importance
print("\nFeature Split Importances:")
print(model.feature_importance())

# 6. Estimate Causal Treatment Effect (Support-Audited G-Computation)
# Audits conditional support for positivity violations (e.g. inpatient vs LOS)
causal_res = model.estimate_effect(
    X=X, 
    primary_col="inpatient", 
    values=[0, 1]
)
print("\nEvent-Weighted Summary HR:", causal_res["hazard_ratio_weighted_mean"])
print("95% Credible Interval:     ", causal_res["hazard_ratio_weighted_ci"])
print("Horizon Risk Difference:   ", causal_res["risk_difference_horizon_mean"])
print("Flagged Support Violations:", causal_res["flagged_support_violations"])

# 7. Continuous Shift Intervention (Modified Treatment Policy: Xi -> Xi + delta)
shift_res = model.estimate_effect(
    X=X,
    primary_col="age",
    delta=5.0  # Shift everyone's age by +5 years
)
print("\nAge +5yr Shift Summary HR: ", shift_res["hazard_ratio_weighted_mean"])
print("Age +5yr Risk Difference:  ", shift_res["risk_difference_horizon_mean"])

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2. Standard Model Presets

SurvBART provides three pre-calibrated factory presets:

Preset	Trees	Draws	Tune	Chains	Thin	Update Fraction	Use Case
preset_standard() (Default)	50	1000	250	4	4 (1,000 final)	1.0 (100%)	Standard scientific benchmark models, publication-grade convergence
preset_fast()	25	500	250	4	5 (400 final)	1.0 (100%)	Fast exploration, interactive debugging, prototyping
preset_high_precision()	100	5000	2000	4	10	1.0 (100%)	High-dimensional data, complex multi-way interactions

# Launch fast exploration model
fast_model = SurvBART.preset_fast(split_method="empirical")

# Or instantiate with explicit custom parameters
custom_model = SurvBART(
    trees=50,
    draws=2000,
    tune=800,
    chains=4,
    thin=10,
    update_fraction=1.0,
    use_sparsity=False,       # Set True to enable DART Dirichlet selection
    split_method="empirical", # Proposes empirical midpoints up to max_cutpoints
    max_cutpoints=512,
    min_node_size=5
)

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3. Step-by-Step Pooled Cross-Section API (Granular Control)

For researchers requiring explicit control over pooled person-time representations:

from bart_survival import (
    SurvBART,
    format_survival_data,
    format_prediction_data,
    compute_survival_curves,
    compute_causal_pdp,
    compute_hazard_ratio
)
from lifelines.datasets import load_rossi

# Load open Rossi recidivism benchmark cohort
rossi = load_rossi()
X_df = rossi[["fin", "age", "race", "wexp", "mar", "paro", "prio"]]
time = rossi["week"].values
status = rossi["arrest"].values

# 1. Format pooled person-time training cross-section
trn = format_survival_data(
    y_time=time,
    y_status=status,
    x=X_df.values,
    time_scale=4.0  # 4-week discrete intervals
)

# 2. Format out-of-sample prediction grid
post_test = format_prediction_data(
    y_time=time,
    y_status=status,
    x=X_df.values,
    time_scale=4.0
)

# 3. Fit SurvBART
model = SurvBART(trees=50, draws=1000, tune=500, chains=2, thin=5)
model.fit(
    y=trn["y"],
    X=trn["x"],
    coords=trn["coord"],
    progressbar=False,
    random_seed=42
)

# 4. Predict out-of-sample hazards and derive cumulative survival curves S(t)
post_preds = model.predict(post_test["post_x"], coords=post_test["coords"])
sv_curves = compute_survival_curves(post_preds, coords=post_test["coords"])

# S(t) shape: (n_draws, n_subjects, n_time_intervals)
print("Survival array shape:", sv_curves["sv"].shape)

---

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Causal G-Computation and Counterfactual Treatment Effects

1. Support-Audited Binary G-Computation

In observational clinical survival studies, counterfactual interventions often suffer from positivity violations or structural zero dependencies. For example, assigning inpatient=0 (outpatient) while leaving length_of_stay=12 days creates non-existent clinical combinations that force tree ensembles into off-support extrapolation.

SurvBART addresses this via estimate_effect() and compute_causal_pdp():

1. Positivity & Support Audit: Automatically scans baseline covariates conditional on exposure levels to identify deterministic zeros, non-overlapping domains, or severe mass-ratio violations.
2. Conditional Donor Resampling: Re-samples dependent features conditional on exposure from observed donor subcohorts while preserving genuine baseline confounders.
3. Counterfactual Predictions: Evaluates both intervention branches through the posterior tree ensemble to yield unconfounded survival curves $S_1(t)$ and $S_0(t)$.

2. Continuous Shift Interventions (Modified Treatment Policies)

For continuous exposures (e.g., biomarker levels, blood pressure, age, radiation dose), arbitrary fixed point comparisons (e.g., $X=1$ vs $X=5$) frequently cause extreme positivity violations. SurvBART supports shift interventions (Modified Treatment Policies): $$\tilde{X}_i = X_i + \Delta \quad \text{vs.} \quad X_i$$ Evaluating a shift preserves empirical covariate correlations and natural population distributions while estimating realistic, policy-relevant counterfactual effects.

3. Hazard Ratios and Risk Differences

model.estimate_effect(...) and compute_hazard_and_risk_difference(...) compute comprehensive effect metrics (always formatted as "Intervention (1) over Reference (0)"):

• Event-Weighted Summary Hazard Ratio (hazard_ratio_weighted_mean): The standard summary HR, calculated as the geometric mean across follow-up intervals weighted by empirical event counts ($w_t = d_t / \sum d_t$). Matches the Cox proportional hazards estimand.
• Time-Varying Hazard Ratio Curve (hazard_ratio_curve_mean): Interval-specific hazard ratio computed via the complementary log-log link: $\mathrm{HR}(t) = \log(1 - \bar{h}_1(t)) / \log(1 - \bar{h}_0(t))$.
• Discrete Risk Ratio (discrete_risk_ratio_mean): Raw, interval-by-interval discrete risk ratio $\bar{h}_1(t) / \bar{h}_0(t)$ without temporal averaging.
• Risk Difference (risk_difference_mean, risk_difference_horizon_mean): Excess cumulative failure risk: $\mathrm{RD}(t) = (1 - S_1(t)) - (1 - S_0(t)) = S_0(t) - S_1(t)$.
• Restricted Mean Survival Time Difference (rmst_difference_mean): Area between the survival curves over the follow-up horizon.

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Documentation

Comprehensive documentation, API references, and mathematical methodology:

• BART-Survival Documentation
• API Reference
• JOSS Paper

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