double-machine-learning

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

Coverage Simulations for DoubleML package

This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.

Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)

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

An implementation of Bayesian Double Machine Learning (BDML) models as per DiTraglia and Liu 2025: https://arxiv.org/abs/2508.12688 and Antonelli et al 2022: https://doi.org/10.1111/biom.13417

Causal effect of contractionary US monetary policy shocks on US employment.

Double/debiased machine learning with instrumental variables (DML-PLIV) and the R-learner, validated against simulated ground truth.

Practical causal inference project evaluating the incremental impact of digital advertising using A/B testing, observational methods, Double Machine Learning, and heterogeneous treatment effects.

Cross-fitted AIPW causal inference with transparent simulation diagnostics

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.