Ask questions in natural language. Discover causal relationships. Estimate treatment effects with 71 methods. Run counterfactual queries via Pearl's SCM engine. All from a single platform.
CausalEdge implements all three rungs of Judea Pearl's causal hierarchy — from observational association to interventional reasoning to counterfactual imagination.
Seeing — What is?
Doing — What if I do?
Imagining — What if I had?
Purpose-built datasets, domain-specific tutorials, and compliance-aware reporting for finance, insurance, and pharmaceutical research.
IFRS 9 ECL, credit risk modeling, portfolio attribution, and regulatory stress testing with causal methods.
IFRS 17 actuarial models, claims analysis, loss reserving, and Solvency II capital modeling with causal methods.
ICH E9(R1) estimands, target trial emulation, real-world evidence, and precision medicine with causal methods.
From data exploration to counterfactual reasoning — every step of the causal inference workflow in one platform.
Ask causal questions in natural language. The AI selects methods, runs analysis, validates assumptions, and explains results.
Interactive DAG editing with collaborative features. Discover structure automatically or build from domain knowledge.
Jupyter-authentic Python execution with complete causal inference toolkit pre-loaded. Full control for data scientists.
Watch your analysis progress in real-time — from data profiling through estimation to validated results.
Automated assumption testing, sensitivity analysis, and refutation tests ensure your causal claims are robust.
One-click comprehensive analysis: data quality, discovery, estimation, validation, and interpretation — automated.
Specialized AI agents collaborate on your analysis — each expert in their domain of causal inference.
Full Level 3 causal reasoning: do-calculus, counterfactual queries, and sequential what-if interventions.
Interactive balance plots, dose-response curves, mediation diagrams, and treatment effect distributions.
The most comprehensive method library available — from classical matching to neural causal models.
Classical propensity-based and doubly robust methods
Causal forests, meta-learners, and cross-fitting approaches
Deep learning architectures for representation-based estimation
Difference-in-differences, synthetic control, and temporal methods
Structure learning algorithms with ensemble consensus
Instrumental variables, regression discontinuity, mediation, and policy
From data exploration to counterfactual reasoning — every step of the causal workflow in a single platform.
Learn causal structure from data — 10+ algorithms with ensemble consensus
Doubly robust methods, ML-based estimation, and neural approaches
Pearl's SCM with do-calculus, abduction, and sequential interventions
Refutation tests, sensitivity analysis, balance diagnostics
Auto-match your data to published benchmarks with method recommendations
Jupyter-authentic Python execution with all causal libraries pre-loaded
Purpose-built AI that goes beyond correlation to discover, estimate, validate, and explain causal relationships.
Ask causal questions in plain English — the AI handles the rest
AI recommends the best estimation method based on your data
Assumption checks, refutation tests, and sensitivity analysis — automatically
Results interpreted in context of your domain and audience
Claims backed by published literature and causal knowledge graph
Every number verified against computation — no fabricated statistics
Your data matched to published benchmarks for context and recommendations
The platform gets smarter with every analysis your team runs
Real Python execution in the browser. Complete causal inference toolkit pre-loaded with your dataset ready to analyze.
# CausalEdge — complete causal inference in Python
from causaledge import CausalEngine
engine = CausalEngine(random_state=42)
# Estimate treatment effect (AIPW — doubly robust)
result = engine.estimate_effect(
data=df, treatment="treat", outcome="re78",
method="aipw", covariates=covariates,
)
print(f"ATE: {result.ate:.4f}, p={result.p_value:.4f}")ATE: 1586.9868, p=0.0402 *Sign in and start analyzing causal relationships in seconds.
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