Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.
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Updated
Sep 8, 2023 - R
Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.
This repo contains all replication files for my M.Sc thesis on "Machine Learning Methods to estimate treatment effects with multivalued treatment".
Code of diploma thesis "Study of Causal Machine Learning Techniques on Data from IoT Applications"
This is the public repository of the code implementation for KCRL.
An educational Python-based introduction to causal inference techniques using machine learning.
scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python
Robust Smooth Heterogeneous Treatment Effect Estimation using Causal Machine Learning
Causal segmentation: estimating conditional average treatment effects for the heterogeneous groups in a sample
Explore the impact of discounts and tech support on revenue through Causal ML models. This repo provides an analysis notebook, data, and a guide on leveraging machine learning for strategic business decisions.
2023학년도 2학기 경기변동론 프로젝트 페이지
We perform market regime detection by testing three deep representation learning models tailored to the SPD Riemannian manifold of correlation matrices constructed from Bloomberg JSE Top 60 traded stock price returns data and synthetically-generated block hierarchical correlation matrices.
Basic experimental set-up for the comparison of causal structure learning algorithms as shown in "Beware of the Simulated DAG".
Causal Machine Learning project analyzing and evaluating different Double ML models for estimating treatment effects in observational data.
Code for Causal GAIL. "Learning human driving behaviors with sequential causal imitation learning", AAAI-22.
Treatment evaluation in presence of large number of covariates or treatment heterogeneity through Machine Learning methods
ImpactFlow is a Python Library for decision modeling based on causal decision models - in which levers and external factors of decisions feed into outcomes.
Collection and implementation of a variety of machine learning code examples (notebooks and Python scripts) and projects.
This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.
Official implementation for ICML23 paper: Which Invariance Should We Transfer? A Causal Minimax Learning Approach
Causal Discovery with Prior Knowledge
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