DoubleML - Double Machine Learning in Python
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 - Double Machine Learning in R
Applied machine learning toolkit implementing Double Machine Learning for Energy Analytics.
Taking causal inference to the extreme!
Causalis - State-of-the-art robust causal inference for experiments and observational data in python
Sensitivity analysis tools for causal ML
Plain-language guide to causal inference for microbiome & multi-omics: DAGs/backdoor, confounders vs mediators/colliders, causal mediation (ACME/ADE/Total), and Double Machine Learning (DML) with toy examples + code.
DoubleML-Serverless - Distributed Double Machine Learning with a Serverless Architecture
This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.
Coverage Simulations for DoubleML package
Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
The repository provides state-of-arts machine-learning approaches to revamping firm fixed effects models in finance studies.
Master's degree thesis project using Debiased Machine Learning to estimate treatment effects from economic policy in US funds performance.
Causal Forest DML analysis of racial approval penalties in U.S. mortgage lending | 42M HMDA applications, 2020-2024 | Under review at Journal of Financial Services Research
Double/debiased machine learning with instrumental variables (DML-PLIV) and the R-learner, validated against simulated ground truth.
An implementation of Bayesian Double Machine Learning (BDML) as per DiTraglia and Liu 2025: https://arxiv.org/abs/2508.12688.
Open-source causal-multimodal engine for creative attribution. Answers why a creative works — not just which one performed better — using Gemini Embedding 2, DoWhy, and EconML.
The repository is for the publication at: Duong K (2024) What really matters for global intergenerational mobility? PLoS ONE 19(6): e0302173.
Code for 'Estimating Treatment Effects with Independent Component Analysis' (arXiv:2507.16467)
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