#Causal-inference
Showing 49 of 49 repositories tagged #causal-inference, ranked by stars
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.
Uplift modeling and causal inference with machine learning algorithms
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.
Next generation of automated data exploratory analysis and visualization platform.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Python Toolkit for Causal and Probabilistic Reasoning
A Python library that helps data scientists to infer causation rather than observing correlation.
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
Learn about Machine Learning and Artificial Intelligence
A Python package for modular causal inference analysis and model evaluations
:exclamation: uplift modeling in scikit-learn style in python :snake:
DoubleML - Double Machine Learning in Python
Must-read papers and resources related to causal inference and machine (deep) learning
Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。
We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.
Python package for causal discovery based on LiNGAM.
Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.
A curated list of AI tools, libraries, and resources for economics research, teaching, and policy analysis. Maintained by the OpenEcon team.
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.
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.
A resource list for causality in statistics, data science and physics
A (concise) curated list of awesome Causal Inference resources.
A selection of state-of-the-art research materials on decision making and motion planning.
python implementation of Peng Ding's "First Course in Causal Inference"
DoubleML - Double Machine Learning in R
Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。
Fast and customizable framework for automatic and quick Causal Inference in Python
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.
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.
Streamline a data analysis process
Spells for everyday living, also a book -- Models Demystified -- now available!
Scikit-learn compatible decision trees beyond those offered in scikit-learn
:package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.
Python tools for regression discontinuity designs
Persistent memory for Claude Code — 36 neuroscience mechanisms, 97 papers. Reproducible via `make reproduce`: LongMemEval-S R@10 98.2% / MRR 0.915 (n=500), LoCoMo R@10 91.5% / MRR 0.805 (n=1982), BEAM-100K retrieval-proxy MRR 0.55. Clean-DB, single-process, production recall path. PostgreSQL + pgvector.
A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data
Machine learning based causal inference/uplift in Python
🎯 :closed_book: Targeted Learning in R: A Causal Data Science Handbook
Biologically-inspired persistent memory engine for Claude Code. 26 cognitive subsystems, Hopfield networks, predictive coding, causal discovery, successor representations, all running locally over SQLite.
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Step-by-step causal inference — method selection, assumptions, and robustness checks
가짜연구소 인과추론팀 특강 및 발표자료 모음입니다.
A curated hub of autonomous-research skills & agents — from idea to paper, on autopilot. | 自主科研技能与智能体精选库 —— 从灵感到论文，全程自动驾驶。
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图
Operational database for AI agents , causal lineage (why()), semantic memory, live dashboards, MCP + Python/TypeScript SDK. Self-host or use db.zizka.ai.