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
Automated Double Machine Learning (DoubleML + AutoML) pipeline for causal inference — estimate incremental impact, CATE & ROI of promotions, pricing and media.
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
Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)
This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.
Cross-fitted AIPW causal inference with transparent simulation diagnostics
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
Practical causal inference project evaluating the incremental impact of digital advertising using A/B testing, observational methods, Double Machine Learning, and heterogeneous treatment effects.
Causal effect of contractionary US monetary policy shocks on US employment.
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
2023학년도 2학기 경기변동론 프로젝트 페이지
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