#causal-inference (19 Repositories)
Ranked open-source repositories tagged with #causal-inference, scored by pull request acceptance likelihood and maintainer engagement velocity.
53.4%
66.1h
19 repositories tagged #causal-inference
pymc-labs/pathmc
Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.
pymc-labs/CausalPy
A Python package for causal inference in quasi-experimental settings
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
pgmpy/pgmpy
Python Toolkit for Causal and Probabilistic Reasoning
igerber/diff-diff
Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.
JuliaDynamics/Associations.jl
Algorithms for quantifying associations, independence testing and causal inference from data.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
mschauer/CausalInference.jl
Causal inference, graphical models and structure learning in Julia
DoubleML/doubleml-for-r
DoubleML - Double Machine Learning in R
r-causal/ggdag
:arrow_lower_left: :arrow_lower_right: An R package for working with causal directed acyclic graphs (DAGs)
pedrohcgs/claude-code-my-workflow
A ready-to-fork Claude Code template for academics using LaTeX/Beamer + R. Multi-agent review, quality gates, adversarial QA, and replication protocols.
brycewang-stanford/StatsPAI
StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.
DoubleML/doubleml-for-py
DoubleML - Double Machine Learning in Python
mmethodz/dreamgraph
DreamGraph is a graph-governed conceptual development environment (CDE) that turns plans, architecture decisions, and project knowledge into auditable execution through a persistent cognitive graph.
ZhenHaoFu810/StataFlow
Stata-aligned econometrics for Python, with field-level validation across the documented support surface.
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
zalando/expan
Open-source Python library for statistical analysis of randomised control trials (A/B tests)
hanlulong/awesome-ai-for-economists
A curated list of AI tools, libraries, and resources for economics research, teaching, and policy analysis. Maintained by the OpenEcon team.
plasma-umass/coz
Coz: Causal Profiling