Open-source tooling for
causal builders
Causal-Copilot sits beneath the Abel Platform as the open-source toolkit for causal discovery workflows.
The most comprehensive open-source causal discovery library. Created by Biwei Huang (Abel co-founder, UC San Diego Professor, creator of causal-learn). The general backend of Abel's Social Physical Engine.
Abel runs GPU-accelerated PCMCI — a core algorithm in this library — daily across 200K+ variables to power its live causal world model.
Algorithm Library
The most comprehensive causal discovery toolkit
Organized by methodology. Abel's algorithm selector automatically chooses the best method for your data.
Constraint-Based
Test conditional independence to discover causal structure.
Score-Based
Search for the causal graph that best fits the data.
Functional
Exploit functional asymmetries to determine causal direction.
Hybrid & Other
Combine constraint and score methods, or use deep learning.
Causal Inference
6 methods for estimating causal effects
Once you discover the graph, use these methods to estimate how much X causes Y.
Semiparametric estimation of causal effects with machine learning nuisance models.
Robust to misspecification of either outcome or treatment model.
Handle unmeasured confounders using instrumental variable estimation.
Estimate heterogeneous treatment effects across subpopulations.
Match treated/control units on propensity scores for effect estimation.
Inverse probability weighting for unbiased causal effect estimation.
Automatic Selection
11-dimensional algorithm selector
Don't know which algorithm to use? Neither does the average data scientist. Abel's selector benchmarks 200+ scenarios and picks the optimal method for your data characteristics.
Used by researchers worldwide
Created by the person who built the tools the field uses. Causal-Copilot powers research at top universities and is the foundation of Abel's $2M+/year GPU infrastructure.
Start making decisions with
live causal intelligence.
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causal intelligence? We're hiring.
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